Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimal Mutation Rates in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Walsh analysis, epistasis, and optimization problem difficulty for evolutionary algorithms
Walsh analysis, epistasis, and optimization problem difficulty for evolutionary algorithms
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
Optimal fixed and adaptive mutation rates for the leadingones problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Optimizing monotone functions can be difficult
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
The computational complexity of N-K fitness functions
IEEE Transactions on Evolutionary Computation
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Exact computation of the expectation curves for uniform crossover
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Problem understanding through landscape theory
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Adaptation of a multiagent evolutionary algorithm to NK landscapes
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Fitness function distributions over generalized search neighborhoods in the q-ary hypercube
Evolutionary Computation
Hi-index | 0.00 |
When the epistasis of the fitness function is bounded by a constant, we show that the expected fitness of an offspring of the (1+1)-EA can be efficiently computed for any point. Moreover, we show that, for any point, it is always possible to efficiently retrieve the "best" mutation rate at that point in the sense that the expected fitness of the resulting offspring is maximized. On linear functions, it has been shown that a mutation rate of 1/n is provably optimal. On functions where epistasis is bounded by a constant k, we show that for sufficiently high fitness, the commonly used mutation rate of 1/n is also best, at least in terms of maximizing the expected fitness of the offspring. However, we find for certain ranges of the fitness function, a better mutation rate can be considerably higher, and can be found by solving for the real roots of a degree-k polynomial whose coefficients contain the nonzero Walsh coefficients of the fitness function. Simulation results on maximum k-satisfiability problems and NK-landscapes show that this expectation-maximized mutation rate can cause significant gains early in search.