When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Phase transitions and symmetry breaking in genetic algorithms with crossover
Theoretical Computer Science
Finding critical backbone structures with genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Improving Performance in Combinatorial Optimisation Using Averaging and Clustering
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Benefits of a population: five mechanisms that advantage population-based algorithms
IEEE Transactions on Evolutionary Computation
Learning the large-scale structure of the MAX-SAT landscape using populations
IEEE Transactions on Evolutionary Computation
Evolutionary computation and its applications in neural and fuzzy systems
Applied Computational Intelligence and Soft Computing
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Argues that the performance of evolutionary algorithms working on hard optimization problems depends strongly on how the population breaks the "symmetry" of the search space. The splitting of the search space into widely separate regions containing local optima is a generic property of a large class of hard optimization problem. This phenomenon is discussed by reference to two well studied examples, the Ising perceptron and the satisfiability problem (K-SAT). A finite population will quickly concentrate on one region of the search space. The cost of crossover between solutions in different regions of search space can accelerate this symmetry breaking. This, in turn, can dramatically reduce the amount of exploration, leading to suboptimal solutions being found. An analysis of symmetry breaking using diffusion model techniques borrowed from classical population genetics is presented. This shows how symmetry breaking depends on parameters such as the population size and selection rate.