Optimization Problems Solver Using Genetic Algorithms

v1.0.1

Description

Predefined problem
Description

Variables

Variable	Min Value	Max Value	Decimals
				-
				+

Chromosome Representation

Constants

Constant	Value	Type
		{{ ct }}	-
		{{ ct }}	+

Constraints & Fitness Function

Constraint
	-
	+

(Arrays are 1-based)

Fitness function
(Arrays are 1-based)

 Maximize

Population

Initial Population number ({{ experiment.maxPopulationItems }})

Selection

Selection Type
{{ st }}
Selected Chromosomes ({{ experiment.selectPercentChromosomes }}%)

Crossover

Crossover Type
{{ ct }}

Mutation

Mutation Type
{{ mt }}
Gene mutation probability ({{ experiment.mutationProbability }}%)

Execute Model

Max iterations ({{ experiment.maxIterations }})
Max number of iterations without improvement ({{ experiment.maxIterationsWithoutImprovement }}%)
Speed

Stop reason

Population (Generation {{ experiment.iteration }})

#	Chromosome	Info	Aptitude
{{ $index }}	{{ chromosome.binaryValue }}	{{ chromosome.info }}	{{ chromosome.aptitude | number : 2 }}

Fitness through the generations

Best Results

Gen	Chromosome	Solution	Aptitude
{{ generation.generationNumber }}	{{ generation.chromosome.binaryValue }}	{{ v.name }}: {{ v.value }}	{{ generation.chromosome.aptitude | number : 2 }}