An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
MOPED: a multi-objective parzen-based estimation of distribution algorithm for continuous problems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Particle swarm optimisation of interplanetary trajectories from Earth to Jupiter and Saturn
Engineering Applications of Artificial Intelligence
Multi Agent Collaborative Search based on Tchebycheff decomposition
Computational Optimization and Applications
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In the present paper some metrics for evaluating the performance of evolutionary algorithms are considered. The capabilities of two different optimisation approaches are compared on three test cases, represented by the optimisation of orbital transfer trajectories. The complexity of the problem of ranking stochastic algorithms by means of quantitative indices is analyzed by means of a large sample of runs, so as to derive statistical properties of the indices in order to evaluate their usefulness in understanding the actual algorithm capabilities and their possible intrinsic limitations in providing reliable information.