The fuzzy co-evolution genetic algorithm is applied to clinical nutrition decision-making optimization. By cooperative co-evolutionary algorithm, the decision problem of clinical nutrition is divided into two populati...
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ISBN:
(纸本)9783037855034
The fuzzy co-evolution genetic algorithm is applied to clinical nutrition decision-making optimization. By cooperative co-evolutionary algorithm, the decision problem of clinical nutrition is divided into two populations,which is combined into a complete diet recipe. In this paper, different fuzzy-based definitions of optimality and dominated solution are introduced. The corresponding extension of Co-evolutionary Genetic Algorithm, so-called Fuzzy Co-Evolution Genetic Algorithm (FCEGA), will be presented as well. To verify the usefulness of such an approach, the approach is tested on analytical test cases in order to show its validity. The solutions, provided by the proposed algorithm for the clinical nutrition diet model,are promising when compared with an existing well-known algorithm.
evolutionarymultiobjective optimization (EMO) has been successfully applied to various real-world scenarios with usually two or three contradicting optimization goals. However, several studies have pointed out a grea...
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ISBN:
(纸本)9781450326629
evolutionarymultiobjective optimization (EMO) has been successfully applied to various real-world scenarios with usually two or three contradicting optimization goals. However, several studies have pointed out a great deterioration of computational performance when handling more than three objectives. In order to improve the scalability of multiobjective evolutionary algorithms (MOEAs) onto higher-dimensional objective spaces, techniques using e.g. scalarizing functions and preference-or indicator-based guidance have been proposed. Most of those proposals require a-priori information or a decision maker during optimization, which increases the complexity of the algorithms. In this paper, we propose a divide and conquer method for many-objective optimization. First, we partition a problem into lower-dimensional subproblems for which standard algorithms are known to perform very well. Our key improvement is the sequential usage of MOEAs, utilizing the results of one suboptimization as initial population for another MOEA. This technique allows modular optimization phases and can be applied to common evolutionaryalgorithms. We test our enhanced method on the hard to solve multiobjective Quadratic Assignment Problem (mQAP), using a variety of established MOEAs.
In this paper, we propose an evolutionary algorithm for handling many-objective optimization problems called MyO-DEMR (many-objective differential evolution with mutation restriction). The algorithm uses the concept o...
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ISBN:
(纸本)9781450319638
In this paper, we propose an evolutionary algorithm for handling many-objective optimization problems called MyO-DEMR (many-objective differential evolution with mutation restriction). The algorithm uses the concept of Pareto dominance coupled with the inverted generational distance metric to select the population of the next generation from the combined multi-set of parents and offspring. Furthermore, we suggest a strategy for the restriction of the difference vector in DE operator in order to improve the convergence property in multi-modal fitness landscape. We compare MyO-DEMR with other state-of-the-art multiobjective evolutionary algorithms on a number of multiobjective optimization problems having up to 20 dimensions. The results reveal that the proposed selection scheme is able to effectively guide the search in high-dimensional objective space. Moreover, MyO-DEMR demonstrates significantly superior performance on multi-modal problems comparing with other DE-based approaches.
In this paper, we introduce a new index for evaluating the interpretability of Mamdani fuzzy rule-based systems (MFRBSs). The index takes both the rule base complexity and the data base integrity into account. We disc...
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ISBN:
(纸本)9781424447350
In this paper, we introduce a new index for evaluating the interpretability of Mamdani fuzzy rule-based systems (MFRBSs). The index takes both the rule base complexity and the data base integrity into account. We discuss the use of this index in the multi-objective evolutionary generation of MFRBSs with different trade-offs between accuracy and interpretability. The rule base and the membership function parameters of the MFRBSs are learnt concurrently by exploiting an appropriate chromosome coding and purposely-defined genetic operators. Results on a real-world regression problem are shown and discussed.
Designing a low-budget lightweight motorcycle frame with superior dynamic and mechanical properties is a complex engineering problem. This complexity is due in part to the presence of multiple design objectives-mass, ...
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Designing a low-budget lightweight motorcycle frame with superior dynamic and mechanical properties is a complex engineering problem. This complexity is due in part to the presence of multiple design objectives-mass, structural stress and rigidity-, the high computational cost of the finite element (FE) simulations used to evaluate the objectives, and the nature of the design variables in the frame's geometry (discrete and continuous). Therefore, this paper presents a neuroacceleration strategy for multiobjective evolutionary algorithms (MOEAs) based on the combined use of real (FE simulations) and approximate fitness function evaluations. The proposed approach accelerates convergence to the Pareto optimal front (POF) comprised of nondominated frame designs. The proposed MOEA uses a mixed genotype to encode discrete and continuous design variables, and a set of genetic operators applied according to the type of variable. The results show that the proposed neuro-accelerated MOEAs, NN-NSGA II and NN-MicroGA, improve upon the performance of their original counterparts, NSGA II and MicroGA. Thus, this neuroacceleration strategy is shown to be effective and probably applicable to other FE-based engineering design problems.
In this paper we present a hybrid intelligent system for the hydrodynamic design of control surfaces on ships. Our main contribution here is the hybridization of multiobjective evolutionary algorithms (MOEA) and a neu...
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ISBN:
(纸本)9783642023187
In this paper we present a hybrid intelligent system for the hydrodynamic design of control surfaces on ships. Our main contribution here is the hybridization of multiobjective evolutionary algorithms (MOEA) and a neural correction procedure in the fitness evaluation stage that permits obtaining solutions that are precise enough for the MOEA to operate with, while drastically reducing the computational cost of the simulation stage for each individual. The MOEA searches for the optimal solutions and the neuronal system corrects the deviations of the simplified simulation model to obtain a more realistic design. This way, we can exploit the benefits of a MOEA decreasing the computational cost in the evaluation of the candidate solutions while preesrving the reliability of the simulation model. The proposed hybrid system is successfully applied in the design of a 2D control surface for ships and extended to a 3D one.
Diversity distribution plays a significant role in assessing performances of MOEAs, so we proposed LRCD (Left-Right Crowding Distance) in [5] to get a better performance than that of the original NSGA2. There are two ...
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This article provides a short introduction to the evolutionarymultiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary algorithms that have been d...
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This article provides a short introduction to the evolutionarymultiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary algorithms that have been developed, from a historical perspective. In the second part of the article, some representative applications within materials science and engineering are reviewed. In the final part of the article, some potential areas for future research in this area are briefly described.
In test-based problems, commonly solved with competitive coevolution algorithms, candidate solutions (e.g., game strategies) are evaluated by interacting with tests (e.g., opponents). As the number of tests is typical...
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ISBN:
(纸本)9783319107622;9783319107615
In test-based problems, commonly solved with competitive coevolution algorithms, candidate solutions (e.g., game strategies) are evaluated by interacting with tests (e.g., opponents). As the number of tests is typically large, it is expensive to calculate the exact value of objective function, and one has to elicit a useful training signal (search gradient) from the outcomes of a limited number of interactions between these coevolving entities. Averaging of interaction outcomes, typically used to that aim, ignores the fact that solutions often have to master different and unrelated skills, which form underlying objectives of the problem. We propose a method for on-line discovery of such objectives via heuristic compression of interaction outcomes. The compressed matrix implicitly defines derived search objectives that can be used by traditional multiobjective search techniques (NSGA-II in this study). When applied to the challenging variant of multi-choice Iterated Prisoner's Dilemma problem, the proposed approach outperforms conventional two-population coevolution in a statistically significant way.
A recent trend in multiobjective evolutionary algorithms is to increase the population size to approximate the nondominated solution set with high accuracy. And the execution time becomes a problem in engineering appl...
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ISBN:
(纸本)9781450349390
A recent trend in multiobjective evolutionary algorithms is to increase the population size to approximate the nondominated solution set with high accuracy. And the execution time becomes a problem in engineering applications. In this paper, we propose distributed, high-speed NSGA-II using a many-core environment to obtain a Pareto-optimal solution set excelling in convergence and diversity. This method improves performance while maintaining the accuracy of the Pareto-optimal solution set by repeating NSGA-II distributed processing in a many-core environment inspired by the divide-and-conquer method together with migration processing for compensation of the nondominated solution set obtained by distributed processing. On comparing with NSGA-II executing on a single CPU and parallel, high-speed NSGA-II using a standard island model, it was found that the proposed method greatly shortened the execution time for obtaining a Pareto-optimal solution set with equivalent hypervolume while increasing the accuracy of solution searching.
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