The Optimum design of golf shaft is carried out using explicit time integration FEM analysis and mathematical programming technique. The 3 dimensional motion of the grip is measured using motion sensor and used for the input of FEM, and shaft is divided into several segment, and bending rigidity and torsional rigidity of each segment is taken to be the design variables. The speed of club head is taken as objective function to maximize. Legendre function is used to solve the discontinuity of the optimal solution. Parreto solution is also introduced to solve the double objective function.

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What are the features of the optimal punted kick in rugby? What is the best way to achieve satisfactory kicks? The objective of this paper is to provide the answers to these questions by optimizing the initial velocity and angular velocity vectors as well as the initial Euler angles for the punted kick. Three objective functions are considered, two of which are the number of extrema in the forward and the lateral directions. The third objective function is the hang time. For optimization all the objective functions must be maximized. The optimization was carried out by using an elitist non-dominated sorting genetic algorithm. The results are summarized as follows: in order to make the hang time longer, the initial pitch angle should be about 90°like a rocket. In the phase of rising, the smaller angle between the longitudinal axis of the ball and the direction of the flight path is preferable. In the phase of falling, the larger angle is preferable. In order to fluctuate the ball more frequently, the spin should be given to change the direction of the side force.

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This paper has introduced the CAE into the ergonomics design to evaluate the human feelings numerically, and into the design optimization of beverage containers. Several design examples in developing aluminum beverage cans and bottles are presented. A couple of examples for the design optimization include shape designs of can end and bottom against the internal pressure as well as column non-linear buckling. Some examples of ergonomics design describe a design of the tab of the can with better finger access and a design of rib-shape embossed bottles for hot-vending, which requires the analyses of tactile sensation of heat as well as elasto-plastic analysis of formability of rib-shape embossed bottles.

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In this paper, a method to create draw-beads, or embossed ribs for improving the rigidity or the strength of thin plate structures is presented. Draw-beads are self-created based on a natural creation law without any artificial operations such as the combination of shape basis vectors. By the creation law local initial curvatures are applied to all finite elements in proportion to the amount of local index in the perpendicular direction of the principal stress direction for resisting local bending moments. Orthogonal material property is used to apply the local curvatures. As the local index, we use the strain energy density in the rigidity design, and the maximum principal stress in the strength design. The thickness is assumed to be constant and the volume is constrained to a specified value. By repeating the stress analysis to evaluate the local index and the shape variation analysis to create the draw beads alternately, the compliance or the maximum stress is reduced. The validity of this numerical method to create the draw-beads for improving the rigidity or the strength of thin plate structures is verified through examples.

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dMSXF and dMSMF are promising methods in combinatorial problems, which perform deterministic multi-step searches using a neighborhood structure and a distance measure. dMSXF generates offspring in the interpolation domain focusing on inheritance of parents' characteristics. In contrast, dMSMF, which has been proposed as a complementary search of dMSXF searches in the extrapolation domain to acquire characteristics which are not appeared in the parents. In previous research, it became clear that dMSXF+dMSMF performed well on both TSP and JSP which have structural differences between their landscapes. In this paper, we applied dMSXF and dMSXF+dMSMF to QAP due to its structural simplicity. Through the experiments, dMSXF was shown to be high availability and incorporation of dMSMF into dMSXF improved their effective search performances on QAP instances.

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Simulated Annealing Programming(SAP) is a method of automatic programming, which extended Simulated Annealing(SA) so that a tree structure could be treated as a solution of optimization problem. Because of the mechanism of accepting bad solutions probabilistically in the optimization process, SAP can generate the optimal solution without lapsing into local solution. In order to obtain the global optimization solution, SAP needs appropriate temperature schedule, and it requires much computational cost to determine the appropriate temperature schedule. In this research, we propose the method to automatically determine a appropriate temperature schedule. In the proposed method, a temperature schedule is determined based on a history of the acceptance rate. Through the numerical experiments, We found that the proposed method provided an effective temperature schedule.

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The robust optimization methods generally apply the two-stage design method which the Taguchi method and the Design For Six Sigma (DFSS) represented. In the two-stage design, the first stage of DFSS is optimized by nominal design variables. At its second stage, initial design values of the robust optimization are defined with optimal solution of previous stage, and the optimization is performed. If robust optimal solution doesn't exist at neighborhood of its initial design values, the two-stage design method doesn't function effectively. Furthermore, a difficulty of the robust optimization increases, if design problem contains discrete or mixed variables. In this paper, to overcome its difficulty the robust optimization method is proposed by using SA and MVFOSM based on one-stage design method. The proposed method is applied to some test problems to check its performance. Furthermore, the validity of the proposed method is discussed through the optimization of space satellite system.

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To obtain the optimal shape of a 3D object minimizing the fluid drag, an adjoint variable method based on the variational principle was formulated and applied to the finite element method. The optimality condition of the method consists of the state equation, the adjoint equation, and the sensitivity equation. To overcome technical problems (e.g., heavy computational tasks and large memory requirements), the present 3D optimization system was developed with the data compression technology supplied by the software library HPC-MW. Utilizing HPC-MW in developing the shape optimization software, the number of program lines, the data compression and the parallel computing were reduced by about 60%. The development period was also dramatically shortened. The parallel computing performance was successfully inherited from HPC-MW. Comparing to the initial shape under stokes flow conditions, the fluid drag on the optimized shape can be reduced by about 25%. The optimal shape given in the present study is in good agreement with that of the Pironneau result.

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An optimization problem of both-ends-clamped beam which is subjected to a concentrated load is considered. It had been shown in the previous report that the actual most suitable form within the same volume exists only under a certain specific condition that a load was in a limited central region of the span. In this paper, an iterative method that uses numerical integration is adopted. Through the comparison with the analytical solution, the characteristic of the optimum solution is discussed. It is found that there exist solutions in which the the deflection angle is discontinuous at the hinge points.

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This paper deals with a shape design of a flexible robot arm. When operating a robot arm, saving energy is an important problem. Lightweighting of robot arm is one method for the energy saving. But lightweighting causes the decrease in the rigidity of the robot, and its work performance deteriorates by elastic vibration. These two items generally have a relation of trade-off. Therefore the lightweighting that consists of vibration suppression is required. To achieve this purpose, we think about multipurpose optimization considering the following evaluation items ; structural weight, tip displacement and total control input.

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Friction and restitution are varying on the road surface, and both coefficients may depend on the location on the road. Humanoid robots that do not have sensing devices can not walk steadily and will slip. However, it is worth pointing out, that some robots walk on different kinds of floors with changing coefficients even if they have no feedback. In this paper, we try to optimize the gait for a humanoid robot by using SA, and then re-optimize it based on random coefficients by using the method of robust optimization. In addition, the control system is very easy, and the simulation model is a small and simple robot that is available in stores that sells robots at a low cost.

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Now in the digital design era, the FEM model of the vehicle interior noise such as the road noise is becoming large and there are even models with 100 million of freedom. In the structure investigation, it's ideal to run the optimization analysis, but in fact some change plans from the initial structure are often prepared and the structure is investigated by checking each characteristic. In this case, the re-analysis, which shortens the re-analysis time by using the information about the dynamic characteristic and the structural change obtained from the initial structure model, is effective. Perturbation method and sensitivity analysis are representative examples of them. Each method has a weak point to restrict the scale of the change. So we invented a new method by which the structure can be practically changed without a scale limitation. Here we explain how this method enables to investigate the design more quickly and reduce the iteration times drastically in the

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When a vibration source such as engines and motors is attached to a structure such as a body, rubber mounts are widely used to insulate the vibration. Stiffness and layout of the mounts are major factors which determine the performance of the vibration insulation. Thus many studies on optimum design of rubber mounts have been reported. Most of them, however, optimize their stiffness and position for a given number of mounts. In a previous report the authors presented an optimization method of rubber mount layout which determines the optimal number of mounts as well as their stiffness and position. In this report the method is applied to optimization of engine mount layout for an automobile. Here dynamics of the automobile is considered in the optimization. Validity of the presented method is confirmed by a numerical example.

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Until now, the analysis method and the optimization method, which were used in design, were evolving independently. However, when designed combining them, coexistence of time and precision was difficult. Then, we have developed the high-speed first order design method. In this paper, we reconsidered optimization method for our design system. We applied agent-oriented optimization method for "heat conduction problem among devices". In a case study, we achieved more than 10% improvement in pareto curve. We also achieved about 90% shorter design time than former result of this study.

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In this paper, a magnetic field optimization problem of a reactor is shown. Reactors are widely used as a part of electric power system. The noise arised from vibration of them which is caused by magnetic force is required to be suppressed. In order to evaluate the noise, the sound pressure is calculated by coupling analysis of magnetic field and structure. Optimization system for minimization the sound pressure using response surface method was developed in this study. Because of the nonlinearity of the response, RBF network was applied to the approximation model. The usefulness of the system was confirmed by the result of an example.

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It is proposed to improve the dynamic model of pantograph in which the degree of freedom is increased and the effects caused by contact wire deviation, a pantograph-head interval and the pitching mode of a pan-head support can be examined. In addition, a technique to improve the compliance characteristics of pantograph such as shifting the natural frequency of a pantograph-head has been proposed, and the model calculation and experiments have confirmed the validity of our proposal. Furthermore, technique for selecting suitable spring constant by using Genetic Algorithm has been proposed.

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In recent years, various types of equipment have become more intelligent. In this research, we propose an intelligent lighting system using infrared communication technology for direct communication between lighting fixtures and illuminance sensors in order to provide the necessary illuminance to a desired location. We actually constructed an experiment simulator based on this concept and verified the effectiveness of the newly developed control method. Verification tests were conducted using an optimization algorithm specialized for lighting control, and the results showed that the various illuminance sensors converged to the preset target illuminance in a very short time. We also confirmed that the system can respond adaptively to the movement of illuminance sensors.

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There are a lot pf requirements in design, actually we need to handle them as we would like to draw high performances of those products. I think optimization might be one possibility for that purpose. However, it is not that easy to derive adequate results. Why? And How to deal with that problem. It has been said that Mulit-objective optimization is key technology to that purpose. However, not many researchers are numning deep into it and perform trade-off analysis which is a key technique in MO. One of the reason might be the number of function calls. In this paper, we are going to propose approximate Satisficing method by using Radial Basis Function networks, to overcome this difficulty, and make trade-off analysis as useful hand tool for designers.

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Satisficing Trade-off Method(STM) is one of the effective methods to deal with multi-objective optimization problems(MOOP), solving the optimality conditions exactly. In MOOPs, Multi-Objective Genetic Algorithm(MOGA) has became very popular nowadays, however, it is a kind of heuristic algorithm and no guarantee of optimality on their solutions. In this point of view, STM has significant value in MOOP, especially in quantitative design consideration. In this paper, first we will discuss about STM's numerical characteristics and show our real implementation in general comercial software iSIGHT 10.0. Then, its application and use scenario will be discussed comparing with MOGA.

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One of main issues in multi-objective optimization is to support choosing a final solution from Pareto frontier which is the set of solution to our problem. For generating a part of Pareto frontier closest to an aspiration level of decision maker, not the whole Pareto frontier, we propose a method composed of two steps ; i) approximate the form of each objective function by using support vector regression on the basis of some sample data, and ii) generate Pareto frontier to the approximated objective functions based on given the aspiration level. In addition, we suggest to select additional data for approximating sequentially the forms of objective functions by relearning step by step. Finally, the effectiveness of the proposed method will be shown through some numerical examples.

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This paper discusses a perspective of optimal design paradigm under the viewpoints of product design and multidisciplinary design optimization. While optimal design techniques are getting much attention for practical applications, its potential is expected to be spread toward rational design of more complicated and large-scale systems beyond the present framework. In this paper, after the history of product innovation and the development of optimal design is overlooked, the promises and challenges for expanding optimal design paradigm to the design problems of product families and platforms are illustrated. Then, some issues for enhancing and expanding the optimal design paradigm is discussed by referring several addresses on the future of multidisciplinary design optimization, etc. Finally, it is mentioned that the optimality defined in mathematical domain and one defined in language domain should be integrated somehow toward the coming challenges.

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The conceptual design phase contains multiple sources of uncertainty in describing design, and nevertheless the decision-making process at this phase exerts a critical effect upon drawing a successful design. The proposed preference set-based design method, consisting of set representation, set propagation, set modification, and set narrowing, which enables the flexible and robust design while incorporating designer's intention. In contrast to the conventional optimization techniques, this method generates a ranged set of design solutions that satisfy changing sets of performance requirements. This paper presents the applicability of our proposal for obtaining the multi-objective satisfactory design solutions by applying to an automotive front-side frame.

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The Stochastic Schemata Exploiter(SSE) is one of the evolutionary optimization algorithms for solving the combinatorial optimization problems. The SSE can improve the global search ability by maintaining the diversity of the population. In this paper, we present the Cross generational elitist selection SSE(cSSE) algorithms which improves the generation alternation model of the SSE. The SSE and the cSSE are compared with the Minimal Generation Gap(MGG) and the Bayesian Optimization Algolithm(BOA) in 0/1 combinatorial optimization problem in order to discuss their convergence property. As a result, we indicate that cSSE has an excellent convergence property and the global search ability.

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To design crossover operators with high search ability in real-coded Genetic Algorithms, it will be efficient to introduce a mechanism, which can concentrate offspring in regions with a satisfactory evaluation value, to a conventional crossover operator based on the functional specialization hypothesis, which is an efficient guideline for design of crossover operators. Here, we propose a new offspring generation method using Delaunay triangulation. The proposed method is based on Simplex Crossover, which is a typical crossover operator based on the functional specialization hypothesis. In addition, the mechanism to concentrate offspring in regions with a satisfactory evaluation value is introduced by utilizing the Delaunay triangulation. Through numerical examples, the proposed method was shown to be capable of deriving the optimum with a smaller population size and lower number of evaluations than Simplex Crossover.

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The Immune Algorithm (IA), which is optimum method based the human immune system, is similar to Genetic Algorithm (GA). The difference between the two methods is obtainable solution. .It's advantage for IA to gain several solutions at once. In addition, IA can expect to use the rocal rules which ware proposed for GA. On the other hands, IA may have the same demerit as GA, and be hard to deal with many parameters. In this paper, we suggest the improved Immune Algorithm. This method is improved by review of Immune System, and uses both secondary response and response contorol by endocrine system. It's hoped that new added functions affect searching ability. Additionally, the effect may be able to overleap complex parameters. This method is applied to some simple problems that search the extremum in the functions. As the results, it's suggestted the potential of overleaping parameter. The validity of this method is checked.

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Data mining is the process to find some patterns or rules from the large volumes of data automatically. It can be classified into six fields ; classification, estimation, prediction, association rule, clustering and profiling. We focus on the data clustering and classify the similar data into the same clusters. These clusters can be used in the field of preparation of data analysis, finding segmentations in market and so on. To derive the clusters, we apply "Multiobjective clustering with automatic determination of the number of clusters (k): MOCK". MOCK uses two complementary objectives based on cluster compactness and connectedness, and returns a set of different trade-off partitioning over a range of different cluster numbers, k. It is able to find the appropriate number of clusters based on the information of the trade-off curve. In this paper, we proposed the scalable k auto-determination scheme of the number of clusters. The proposed scheme reduced the Pareto-size and usually select adjust number of clusters.

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An optimization procedure to decide layouts and shapes of cooling channels in a plastic injection molding die is presented for a reduction of molded defects in a plastic product. To confirm the validity, the proposed method is applied to the design problems given by different thermal and geometrical conditions. In the proposed method, the design problem of cooling channels, which is the most effective factor to control temperature distribution in the die, is formulated as the multi-objective optimization problem adopted two objective functions. One is to evaluate the temperature distribution in the die for reduction of some defects such as residual stresses, and the other is to measure the cooling rate of injected plastic to improve productivity. To calculate these functions, the transient cooling process of the plastic injection molding is simulated numerically. The design variables are the cooling channel arrangements which represent a complex cooling pipe. The design space is approximated by a response surface based on the design of experiment, which is applied to decide combinations of the design variables, to optimize efficiently under required reliability. An optimum layout of cooling channels is decided by optimizing the response surface.

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In structural designs considering thermal loading, maximization of temperature diffusivity in structures is one of the most important essential factors in reducing operating temperature and maintaining product durability, in addition to the usual maximization of stiffness that optimal designs achieve. In this paper, a topology optimization method is constructed for thermal problems considering generic heat transfer boundaries, including heat convection boundaries, based on the homogenization method. First, the topology optimization method for thermal problems is discussed using a homogenization method that assumes a continuous material distribution. Next, a new objective function that can take into account heat transfer boundaries, such as temperature-constant and heat convection boundaries, is proposed, based on the concept of a total potential energy maximization problem in the structural problem, and an optimization problem is formulated using the proposed objective function. An optimization algorithm is constructed using the Finite Element Method (FEM) and Sequential Linear Programming (SLP). Finally, several numerical examples are presented in order to confirm the usefulness of the proposed method.

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This paper proposes a shape optimization method for obtaining mechanical structures that maximize structural stiffness, based on the level set method. First, the basic details of a shape optimization method using the level set method are briefly explained, and an optimization problem that addresses the minimum mean compliance problem is formulated. Next, an optimization algorithm is constructed based on this formulation. In this optimization scheme, a new geometrical re-initialization method is proposed, in which a level set function is re-initialized, based on a zero level set surface. This geometrical re-initialization method can easily deal with level set function singularities, and can be applied to non-structural meshes. A second new method is also proposed in which the level set function is modified to satisfy a volume constraint while preserving topology of the structure being optimized. By using these two new methods, shape optimization problems can be solved using the finite element method rather than the finite difference method. Finally, several examples are provided to confirm the usefulness of the proposed shape optimization method.

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To design economical structural forms, it is necessary to optimize both the topology and shape of structures. To optimize topology, we propose a hybrid of Genetic Algorithm (GA) and Evolutionary Structural Optimization (ESO). This paper describes the considerations in applying the proposed method to topology structural optimization. Through numerical examples, the proposed method showed better search ability than GA or ESO methods alone. Moreover, this hybrid method makes it possible to design a more economical structural form.

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A general approach is presented for generating link mechanisms from a highly connected ground structure. The structure is modeled as a pin-jointed truss, and the unnecessary members are removed by solving the optimization problem for minimizing the total structural volume under constraints on the maximum load, the displacement at the specified node, and the stiffnesses at initial and final states. The design variables are cross-sectional areas and the nodal coordinates. It is shown in the numerical examples that several mechanisms can be naturally found as a result of optimization starting from randomly selected initial solutions.

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This paper presents an iterative structural design technique that uses local control conditions and the forming stiffness of individual members. Microtruss is modeled as an assemblages of a number of repeating unit cells to undergo finite element analysis in order to feed-back the stress results for the control stiffness of each member. Further iteration enable for the identification of a topology with the use of the local stress depends on step by step. The forming process analysis for the Michell truss problems in 3 dimensional space has proposed technique.

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In this paper a topology optimization algorithm of shell structure subject to crashing deformations is presented. As a topology optimization method the density method is used and the work done by the external loads is employed as the object function. In the density method the element density is taken as the design variable and material properties such as Young's modulus, the tangent modulus or the yield stress are assumed to be power functions of it. The bilinear shell element, which is widely used for the car crash analysis, is employed in the crash analysis with the explicit time integration scheme. The sensitivity analysis is conducted numerically using the history of nodal displacements and the density of the elements are updated with the optimality criteria method. Through several numerical examples it was shown that the present method is useful to design a shell structure which has the largest or a specified amount of energy absorption capacity.

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This study deals with the discrete optimization problem for the least steel structure weight, with member sections selected from available list. A method called Weight-Limited GA(Genetic Algorithm) is proposed to improve the traditional GA, with the crossover under a given limit to the weight of the best combination of previous generation. 10-bar, 11-bar and 17-bar plane trusses and 3-bay 5-story, 2-bay 8-story and 3-bay 12-story plane frames are taken as examples. Exact solutions of them are obtained by enumeration algorithm. GA and proposed method are performed on these examples simultaneously. Results have shown that the proposed method is valid in improving the convergence rate and actuate ratio.

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A global optimization method is presented for computing the minimum limit load factor of trusses subjected to unknown but bounded loads. We assume that the external forces consist of a part proportional to a load factor and a part that is uncertain around its nominal value. The worst-case limit load factor is introduced as the smallest limit load factor realized with some uncertain parameters. By reformulating the worst-case determination problem as a mixed 0-1 programming problem, we propose a global optimization algorithm by a combination of a branch-and-bound method based on the linear programming relaxations and a cutting plane method based on the disjunctive cuts.

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An efficient approach for generating pin-jointed compliant mechanisms is presented. A compliant mechanism uses the elastic deformation of structural parts to realize the mechanism for shape transformation of the entire structure, which is contrary to the conventional link mechanism. Ohsaki and Nishiwaki (Struct. Multidisc. Optim., Vol.30, pp.327-334, 2005) presented a method for generating flexible multistable bar-joint mechanisms using nonlinear programming approach. However, due to highly nonlinear property of the problem, the nonlinear programming problem should be solved many times with random initial solutions to obtain several types of mechanisms. Since the compliant pin-jointed mechanism is usually statically determinate, the optimization problem can be solved easily if the design space is limited to statically determinate structures. Avis et al. (Proc. of COCOON 2006, LNCS 4112, pp.205-215, 2006) presented an algorithm for enumerating all the non-crossing generically minimally rigid bar-joint frameworks, which are regarded as statically determinate trusses in structural engineering. In this paper, bistable mechanisms utilizing snapthrough behavior are obtained more efficiently with the algorithm. In the numerical example, many types of bistable compliant mechanisms are generated.

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In this paper we present an algorithm for enumerating without repetitions all the non-crossing generically minimally rigid bar-and-joint frameworks (also called non-crossing Laman frameworks) on a given generic set of n points. Our algorithm is based on the reverse search paradigm of Avis and Fukuda. It generates each output graph in O(n^4) time and O(n) space, or, with a slightly different implementation, in O(n^3) time and O(n^2) space. In particular, we obtain that the set of all non-crossing Laman frameworks on a given point set is connected by flips which remove an edge and then restore the minimally rigid property with the addition of a non-crossing edge.

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When we apply the optimization algorithms to the target problems, the selection of an optimization algorithm significantly influences the accuracy of solutions. Therefore, we have to select the appropriate algorithms according to the complexity of problems. In this, we assume the total simulation is given by the users. Then, in our strategy to choose the optimization, there are two features ; 1) the maximum time to execute the optimization algorithms is defined, and the purpose of the strategy is to obtain the better solutions for the limited time, and 2) the landscape of the search space can be roughly grasped after the optimization has been finished. In this paper, we describe the algorithms used for the proposed strategy: SQP, GA and DIRECT. The searching ability of each algorithm is also discussed. We illustrated the strategy template how to use these three algorithms.

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Vehicle of Formula-SAE have intake and exhaust system of a particular layout. This system is required by the competition rule to be placed in the ristricter between the throttle and the tank. Therefore, there are little information document and data available. It is difficult to decide the intake pipe length and capacity of the surge tank and shape. In this study, we tried to apply multidisciplinary analysis consisting of 3DCFD and 1D engine performance analysis, moreover multidisciplinary optimization. An unpredictable part of this engine is made clear by using of the results of these simulations.

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Logistics optimization has been noted increasingly as a key issue to improve the business efficiency under global competition. So far studies have concerned with rather simple problems and major emphasizes have been paid to developing a new algorithm, and to evaluating its validity through benchmarking. However, easy applications following these outcomes often cause a dramatic increase in problem size, and hence in such difficulty that makes impossible to solve the resulting problem by any currently available software. We need to make more earnest efforts to cope with the complex and complicated real-world situations. With this point of view, in this study, we concern with the hierarchical logistic network from two aspects that have been scarcely considered previously though they are very popular in real-world. We consider there a mufti-commodity product problem and a problem with stair-wised discount cost of transportation. To cope with these conditions, we have developed extended methods of the hybrid meta-heuristic approach for each problem. Novel iterative procedures are invented respectively while keep applying the graph algorithm towards the high speed solution of the lower level problem of the decomposed original problem. Validity of the former method is verified through comparison with the commercial software while we implement the algorithm as software amenable for supporting a daily logistic planning, and confirm its effectiveness through a real-world application for the latex.

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This study deals with the management optimization and risk evaluation for gaming shops by considering about profit or loss from gaming and simulations. And it is investigated that this gaming model structures are based on systems reliability theoretical approach. Although there are multi kinds of many games or these shops about their gaming or simulations all over the world, in this study it is treated to use the behaviors about the variance spreading of gaming simulations based on each probabilistic phenomenon from view points of the service by the gaming supplier and the life cycle for its players. In addition it is examined that the characteristics of these estimations of maximum risk with gain by winner among players. Moreover, it is able to be suggested that the design optimization method about loss or risk throughout gaming simulation.

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In this research, we verify the effectiveness of Simulated Annealing(SA) in program search by paying attention to the temperature parameter which controls the transition probability of bad moves. It is found that the temperature can control the bloat which is the big problem in Genetic Programming(GP). The fixed temperature search with important temperature instead of conventional cooling schedule provided the same performance as GP without the occurring bloat.

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