Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing (IEEE Press Series on Computational Intelligence)
Selecting for evolvable representations
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Information Sciences: an International Journal
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Information Sciences: an International Journal
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
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Evolutionary computation plays a principal role in intelligent design automation. Evolutionary approaches have discovered novel and patentable designs. Nonetheless, evolutionary techniques may lead to designs that lack robustness. This critical issue is strongly connected to the concept of evolvability. In nature, highly evolvable species tend to be found in rapidly changing environments. Such species can be considered robust against environmental changes. Consequently, to create robust engineering designs it could be beneficial to use variable, rather than fixed, fitness criteria. In this paper, we study the performance of an evolutionary programming algorithm with periodical switching between goals, which are selected randomly from a set of related goals. It is shown by a dual-objective filter optimization example that altering goals may improve evolvability to a fixed goal by enhancing the dynamics of solution population, and guiding the search to areas where improved solutions are likely to be found. Our reference algorithm with a single goal is able to find solutions with competitive fitness, but these solutions are results of premature convergence, because they are poorly evolvable. By using the same algorithm with switching goals, we can extend the productive search length considerably; both the fitness and robustness of such designs are improved.