Niche Genetic Algorithms (NGA) are a special category of Genetic Algorithms (GA) that solve problems with multiple optima. These algorithms preserve genetic diversity and prevent the GA from converging on a single optima. Many NGAs suffer from the Niche Radius Problem (NRP), which is the problem of correctly setting a radius parameter for optimal results. While the selection of the radius value has been widely researched, the effects of other GA parameters on genetic diversity is not well known. This research is a parameter sensitivity analysis on the other parameters in a GA, namely mutation rate, number of individuals and number of generations
In general, biologically-inspired multi-objective optimization algorithms comprise several parameter...
International audienceThis paper considers a new method that enables a genetic algorithm (GA) to ide...
International audienceThis paper considers a new method that enables a genetic algorithm (GA) to ide...
AbstractWhen Genetic Algorithms (GA) are used to solve layout problems, the solution quality may be ...
AbstractThis paper introduces a niching technique called GAS (S stands for species) which dynamicall...
In this paper, a continuation of a variable radius niche technique called Dynamic Niche Clustering d...
Genetic Algorithms are powerful tools, which when set upon a solution space will search for the opti...
This thesis is concerned with using genetic algorithms to investigate ecological niche concepts. A G...
Describes Niche Search, a genetic-based optimisation approach which is characterised by an evolution...
This thesis is concerned with using genetic algorithms to investigate ecological niche concepts. A G...
The problem of multimodal functional optimization has been addressed by much research producing many...
The problem of multimodal functional optimization has been addressed by much research producing many...
Genetic algorithms (GA) are applied to the solution of high-dimensional optimization problems. Addit...
Some non-linear optimisation problems are difficult to solve by con-ventional hill-climbing methods,...
In general, biologically-inspired multi-objective optimization algorithms comprise several parameter...
In general, biologically-inspired multi-objective optimization algorithms comprise several parameter...
International audienceThis paper considers a new method that enables a genetic algorithm (GA) to ide...
International audienceThis paper considers a new method that enables a genetic algorithm (GA) to ide...
AbstractWhen Genetic Algorithms (GA) are used to solve layout problems, the solution quality may be ...
AbstractThis paper introduces a niching technique called GAS (S stands for species) which dynamicall...
In this paper, a continuation of a variable radius niche technique called Dynamic Niche Clustering d...
Genetic Algorithms are powerful tools, which when set upon a solution space will search for the opti...
This thesis is concerned with using genetic algorithms to investigate ecological niche concepts. A G...
Describes Niche Search, a genetic-based optimisation approach which is characterised by an evolution...
This thesis is concerned with using genetic algorithms to investigate ecological niche concepts. A G...
The problem of multimodal functional optimization has been addressed by much research producing many...
The problem of multimodal functional optimization has been addressed by much research producing many...
Genetic algorithms (GA) are applied to the solution of high-dimensional optimization problems. Addit...
Some non-linear optimisation problems are difficult to solve by con-ventional hill-climbing methods,...
In general, biologically-inspired multi-objective optimization algorithms comprise several parameter...
In general, biologically-inspired multi-objective optimization algorithms comprise several parameter...
International audienceThis paper considers a new method that enables a genetic algorithm (GA) to ide...
International audienceThis paper considers a new method that enables a genetic algorithm (GA) to ide...