How to solve it: modern heuristics
How to solve it: modern heuristics
An Adaptive Evolutionary Algorithm for Numerical Optimization
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving real-parameter genetic algorithm with simulated annealing for engineering problems
Advances in Engineering Software
Adaptive design optimization of wireless sensor networks using genetic algorithms
Computer Networks: The International Journal of Computer and Telecommunications Networking
An adaptive scheduling system with genetic algorithms for arranging employee training programs
Expert Systems with Applications: An International Journal
Genetic algorithms to solve the cover printing problem
Computers and Operations Research
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
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
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Research of multi-population agent genetic algorithm for feature selection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Two coding based adaptive parallel co-genetic algorithm with double agents structure
Engineering Applications of Artificial Intelligence
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
Expert Systems with Applications: An International Journal
Application of Genetic Algorithm in unit selection for Malay speech synthesis system
Expert Systems with Applications: An International Journal
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In this paper, one novel genetic algorithm dynamic chain-like agent genetic algorithm (CAGA) is proposed for solving global numerical optimization problem and feature selection problem. The CAGA combines the chain-like agent structure with dynamic neighboring genetic operators to get higher optimization capability. An agent in chain-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and can use knowledge to increase energies. Global numerical optimization problem and feature selection problem are the most important problems for evolutionary algorithm, especially for genetic algorithm. Hence, the experiments of global numerical optimization and feature selection are necessary to verify the performance of genetic algorithms. Corresponding experiments have been done and show that CAGA is suitable for real coding and binary coding optimization problems, and has more precise and more stable optimization results.