Neural Computation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Linked interpolation-optimization strategies for multicriteria optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fitness inheritance for noisy evolutionary multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Marks' Standard Handbook For Mechanical Engineers (Standard Handbook for Mechanical Engineers)
Marks' Standard Handbook For Mechanical Engineers (Standard Handbook for Mechanical Engineers)
Multiobjective GA optimization using reduced models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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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.