Let's Get Ready To Rumble: Crossover Versus Mutation Head to Head
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This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for additively separable deterministic problems, the BB-wise mutation is more efficient than crossover, while the crossover outperforms the mutation on additively separable problems perturbed with additive Gaussian noise. The results show that the speed-up of using BB-wise mutation on deterministic problems is O(k½ log m), where k is the BB size, and m is the number of BBs. Likewise, the speed-up of using crossover on stochastic problems with fixed noise variance is O(m·k½logm).
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This study develops a decision-making strategy for deciding between fitness functions with differing bias values. Simple, yet practical facetwise models are derived to aid the decision-making process. The decision making strategy is designed to provide maximum speed-up and thereby enhance the efficiency of GA search processes. Results indicate that bias can be handled temporally and that significant speed-up values can be obtained.
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Empirical Analysis of ideal recombination on random decomposable problems
Empirical Analysis of ideal recombination on random decomposable problems
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Population sizing for entropy-based model buliding In genetic algorithms
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...
Modeling selection pressure in XCS for proportionate and tournament selection
Modeling selection pressure in XCS for proportionate and tournament selection
Modeling XCS in class imbalances: Population sizing and parameter settings
Modeling XCS in class imbalances: Population sizing and parameter settings
Substructrual surrogates for learning decomposable classification problems: i...
Substructrual surrogates for learning decomposable classification problems: i...
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...
Let's get ready to rumble redux: Crossover versus mutation head to head on ex...
Automated alphabet reduction with evolutionary algorithms for protein structu...
Automated alphabet reduction with evolutionary algorithms for protein structu...
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Modeling selection pressure in XCS for proportionate and tournament selection
Modeling selection pressure in XCS for proportionate and tournament selection
Modeling XCS in class imbalances: Population size and parameter settings
Modeling XCS in class imbalances: Population size and parameter settings
Fast and accurate reaction dynamics via multiobjective genetic algorithm opti...
Fast and accurate reaction dynamics via multiobjective genetic algorithm opti...
Silicon Cluster Optimization Using Extended Compact Genetic Algorithm
Silicon Cluster Optimization Using Extended Compact Genetic Algorithm
A Practical Schema Theorem for Genetic Algorithm Design and Tuning
A Practical Schema Theorem for Genetic Algorithm Design and Tuning
Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algori...
Efficient Cluster Optimization Using A Hybrid Extended Compact Genetic Algori...
Modeling Tournament Selection with Replacement Using Apparent Added Noise
Modeling Tournament Selection with Replacement Using Apparent Added Noise
Evaluation Relaxation as an Efficiency-Enhancement Technique: Handling Varian...
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