Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Genetic Algorithms in Noisy Environments
Machine Learning
Evolution Strategies on Noisy Functions: How to Improve Convergence Properties
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Empirical investigation of multiparent recombination operators in evolution strategies
Evolutionary Computation
Macroevolutionary algorithms: a new optimization method on fitnesslandscapes
IEEE Transactions on Evolutionary Computation
A Fast Evolutionary Algorithm for Image Compression in Hardware
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
The Fast Evaluation Strategy for Evolvable Hardware
Genetic Programming and Evolvable Machines
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Genetic Algorithms (GAs) are a popular and robust strategy for optimisation problems. However, these algorithms often require huge computation power for solving real problems and are often criticized for their slow operation. For most applications, the bottleneck of the GAs is the fitness evaluation task. This paper introduces a fitness estimation strategy (FES) for genetic algorithms that does not evaluate all new individuals, thus operating faster. A fitness and associated reliability value are assigned to each new individual that is only evaluated using the true fitness function if the reliability value is below some threshold. Moreover, applying some random evaluation and error compensation strategies to the FES further enhances the performance of the algorithm. Simulation results show that for six optimization functions, the GA with FES requires fewer evaluations while obtaining similar solutions to those found using a traditional genetic algorithm. For these same functions the algorithm generally also finds a better fitness value on average for the same number of evaluations. Additionally the GA with FES does not have the side effect of premature convergence of the population. It climbs faster in the initial stages of the evolution process without becoming trapped in the local minima.