A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Contemporary Evolution Strategies
Proceedings of the Third European Conference on Advances in Artificial Life
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Immunological Computation: Theory and Applications
Immunological Computation: Theory and Applications
Maximal age in randomized search heuristics with aging
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analysis of diversity-preserving mechanisms for global exploration*
Evolutionary Computation
Experimental analysis of the aging operator for static and dynamic optimisation problems
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
An Immune Algorithm for Protein Structure Prediction on Lattice Models
IEEE Transactions on Evolutionary Computation
Artificial immune systems for optimisation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Artificial immune systems for optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Aging is a general mechanism that is used to increase the diversity of the collection of search points a randomized search heuristic works on. This aims at improving the search heuristic's performance for difficult problems. Examples of randomized search heuristics where aging has been employed include evolutionary algorithms and artificial immune systems. While it is known that randomized search heuristics with aging can be very much superior to randomized search heuristics without aging, recently the point has been made that aging can often be replaced by appropriate restart strategies that are conceptionally simpler and computationally faster. Here, it is demonstrated that aging can achieve performance improvements that restarts cannot. This is done by means of an illustrative example that also involves crossover as an important component. In addition to the theoretical results an empirical study demonstrates the importance of appropriately sized populations