The genetic algorithm (GA) is a popular random search and optimization method inspired by the concepts of crossover, random mutation, and natural selection from evolutionary biology. The real-valued genetic algorithm (RGA) is an improved version of the genetic algorithm designed for direct operation on real-valued variables. In this work, a modified version of a genetic algorithm is introduced, which is called a modified genetic algorithm with micro-movement (MGAM). It implements a particle swarm optimization (PSO)-inspired micro-movement phase that helps to improve the convergence rate, while employing the efficient GA mechanism for maintaining population diversity. In order to test the capability of the MGAM, we first implement it on five...
Abstract---Genetic algorithms represent an efficient global method for nonlinear optimization proble...
Genetic algorithms for mathematical function optimization are modeled on search strategies employed ...
A formalism for modelling the dynamics of genetic algorithms using methods from statistical physics,...
The genetic algorithm (GA) is a popular random search and optimization method inspired by the concep...
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solv...
Many real world problems in science and engineering can be treated as optimization problems with mul...
In the paper, a novel instance of the real-coding steady-state genetic algorithm, called the Mean-ad...
AbstractMany adaptive systems require optimization in real time. Whether it is a robot that must mai...
AbstractCanonical genetic algorithms have the defects of pre-maturity and stagnation when applied in...
An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was dev...
AbstractGenetic algorithm (GA) is a population-based stochastic optimization technique that has two ...
This paper shows a practical application of genetic algo- rithms (GAs) for compensating the local m...
We review different techniques for improving GA performance. By analysing the fitness landscape, a c...
Since its inception in 1975, Genetics Algorithms (GAs) have been successfully used as a tool for glo...
Genetic Algorithms (GA) are a powerful search and optimization technique that can be applied to nume...
Abstract---Genetic algorithms represent an efficient global method for nonlinear optimization proble...
Genetic algorithms for mathematical function optimization are modeled on search strategies employed ...
A formalism for modelling the dynamics of genetic algorithms using methods from statistical physics,...
The genetic algorithm (GA) is a popular random search and optimization method inspired by the concep...
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solv...
Many real world problems in science and engineering can be treated as optimization problems with mul...
In the paper, a novel instance of the real-coding steady-state genetic algorithm, called the Mean-ad...
AbstractMany adaptive systems require optimization in real time. Whether it is a robot that must mai...
AbstractCanonical genetic algorithms have the defects of pre-maturity and stagnation when applied in...
An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was dev...
AbstractGenetic algorithm (GA) is a population-based stochastic optimization technique that has two ...
This paper shows a practical application of genetic algo- rithms (GAs) for compensating the local m...
We review different techniques for improving GA performance. By analysing the fitness landscape, a c...
Since its inception in 1975, Genetics Algorithms (GAs) have been successfully used as a tool for glo...
Genetic Algorithms (GA) are a powerful search and optimization technique that can be applied to nume...
Abstract---Genetic algorithms represent an efficient global method for nonlinear optimization proble...
Genetic algorithms for mathematical function optimization are modeled on search strategies employed ...
A formalism for modelling the dynamics of genetic algorithms using methods from statistical physics,...