Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to genetic algorithms
An introduction to genetic algorithms
How to solve it: modern heuristics
How to solve it: modern heuristics
Co-evolutionary search in asymmetric spaces
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Adaptive Evolutionary Algorithm for Numerical Optimization
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
On classes of functions for which No Free Lunch results hold
Information Processing Letters
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Expert Systems with Applications: An International Journal
Conditions that Obviate the No-Free-Lunch Theorems for Optimization
INFORMS Journal on Computing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two coding based adaptive parallel co-genetic algorithm with double agents structure
Engineering Applications of Artificial Intelligence
A memetic algorithm for the quadratic multiple container packing problem
Applied Intelligence
Information Sciences: an International Journal
Multi-circle detection on images inspired by collective animal behavior
Applied Intelligence
Dynamic bee colony algorithm based on multi-species co-evolution
Applied Intelligence
| Hi-index | 0.00 |
For the low optimization precision and long optimization time of genetic algorithm, this paper proposed a multi-population agent co-genetic algorithm with chain-like agent structure (MPAGA). This algorithm adopted multi-population parallel searching mode, close chain-like agent structure, cycle chain-like agent structure, dynamic neighborhood competition and orthogonal crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. In order to verify the optimization precision of this algorithm, some popular benchmark test functions were used for comparing this algorithm and a popular agent genetic algorithm (MAGA). The experimental results show that MPAGA has higher optimization precision and shorter optimization time than MAGA.