An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Self-adaptation in evolving systems
Artificial Life
Adaptive operator probabilities in a genetic algorithm that applies three operators
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
The theory of evolution strategies
The theory of evolution strategies
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
On Appropriate Adaptation Levels for the Learning of Gene Linkage
Genetic Programming and Evolvable Machines
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Strategy Parameter Variety In Self-adaptation Of Mutation Rates
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Study of Crossover Operators in Genetic Programming
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
An Adaptive Poly-Parental Recombination Strategy
Selected Papers from AISB Workshop on Evolutionary Computing
A Study on the use of "self-generation'' in memetic algorithms
Natural Computing: an international journal
Evolutionary Computation - Special issue on magnetic algorithms
An adaptive pursuit strategy for allocating operator probabilities
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
A review of metrics on permutations for search landscape analysis
Computers and Operations Research
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
Is self-adaptation of selection pressure and population size possible?: a case study
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Self-adaptive mutations may lead to premature convergence
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the log-normal self-adaptation of the mutation rate in binary search spaces
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Scaling up a hybrid genetic linear programming algorithm for statistical disclosure control
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
Polynomial joint angle arm robot motion planning in complex geometrical obstacles
Applied Soft Computing
Initial application of ant colony optimisation to statistical disclosure control
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Geometric-based sampling for permutation optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A comparison of two memetic algorithms for software class modelling
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Static and adaptive mutation techniques for genetic algorithm: a systematic comparative analysis
International Journal of Computational Science and Engineering
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The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one “"optimal choice”"; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via self-adaptation, thus removing this nontrivial task from the GA user and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Self-adaptation has been proven successful for mutation step sizes in the continuous domain, and for the probability of applying bitwise mutation to binary encodings; here we examine whether this can translate to the choice and parameterisation of mutation operators for permutation encodings. We examine one method for adapting the choice of operator during runtime, and several different methods for adapting the rate at which the chosen operator is applied. In order to evaluate these algorithms, we have used a range of benchmark TSP problems. Of course this paper is not intended to present a state of the art in TSP solvers; rather, we use this well known problem as typical of many that require a permutation encoding, where our results indicate that self-adaptation can prove beneficial. The results show that GAs using appropriate methods to self-adapt their mutation operator and mutation rate find solutions of comparable or lower cost than algorithms with “"static”" operators, even when the latter have been extensively pretuned. Although the adaptive GAs tend to need longer to run, we show that is a price well worth paying as the time spent finding the optimal mutation operator and rate for the nonadaptive versions can be considerable. Finally, we evaluate the sensitivity of the self-adaptive methods to changes in the implementation, and to the choice of other genetic operators and population models. The results show that the methods presented are robust, in the sense that the performance benefits can be obtained in a wide range of host algorithms.