Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Finite elements, genetic algorithms and &bgr;-splines: a combined technique for shape optimization
Finite Elements in Analysis and Design
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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This paper details the application of evolutionary strategies on shape optimization problems. Evolutionary algorithms are still rather unpopular methods due to their lack of mathematical foundation. In spite of this, in complex problems, like multi-modal and multi-objective problems, heuristic techniques are able to outperform conventional optimization methods. We give an insight into the framework of evolutionary algorithms and demonstrate their advantages on examples where gradient-based methods proved to be second-best. As is well known, the drawback of these methods is a large number of function evaluations causing huge computational overhead. We show that heuristic techniques yield satisfiable results within reasonable limits of the number of function evaluations. Numerical examples are given.