Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Application of a simple binary genetic algorithm to a noiseless testbed benchmark
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Real-coded genetic algorithm benchmarked on noiseless black-box optimization testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Black-box optimization benchmarking for noiseless function testbed using a direction-based RCGA
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
In this paper, a real-coded genetic algorithm (RCGA) which incorporates an exploratory search mechanism based on vector projection termed projection-based RCGA (PRCGA) is benchmarked on the noisefree BBOB 2013 testbed. It is an enhanced version of RCGA-P in [22, 23]. The projection operator incorporated in PRCGA shows promising exploratory search capability in some problem landscape. PRCGA is equipped with a multiple independent restart mechanism and a stagnation alleviation mechanism. The maximum number of function evaluations (#FEs) for each test run is set to 105 times the problem dimension. PRCGA shows encouraging results on several problems in the low and moderate search dimensions. It is able to solve each type of problem with the dimension up to 40 with lower precision but not all the functions to the desired level of accuracy of 10-8 especially for high conditioning and multi-modal functions within the specified maximum #FEs.