How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Preserving and exploiting genetic diversity in evolutionary programming algorithms
IEEE Transactions on Evolutionary Computation
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Improving evolutionary algorithms with scouting
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Viewing the problem from different angles: a new diversity measure based on angular distances
Journal of Artificial Evolution and Applications
On the performance of fitness uniform selection for non-deceptive problems
Proceedings of the 48th Annual Southeast Regional Conference
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Expert Systems with Applications: An International Journal
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Review of phenotypic diversity formulations for diagnostic tool
Applied Soft Computing
Effective diversity maintenance in deceptive domains
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of fitter individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other hand. Motivated by a universal similarity relation on the individuals, we propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure toward sparsely populated fitness regions, not necessarily toward higher fitness, as is the case for all other selection schemes. We show analytically on a simple example that the new selection scheme can be much more effective than standard selection schemes. We also propose a new deletion scheme which achieves a similar result via deletion and show how such a scheme preserves genetic diversity more effectively than standard approaches. We compare the performance of the new schemes to tournament selection and random deletion on an artificial deceptive problem and a range of NP hard problems: traveling salesman, set covering, and satisfiability