Genetic algorithms (GAs) are powerful tools that allow engineers and scientists to find good solutions to hard computational problems using evolutionary principles. The classic genetic algorithm suffers from the configuration problem, the difficulty of choosing optimal parameter settings. Genetic algorithm literature is full of empirical tricks, techniques, and rules of thumb that enable GAs to...

This paper presents genetic algorithm based solution for determing alignment of multiple molecular sequences. Two datasets from DNA families Canis_familiaris and galaxy dataset have been considered for experimental work & analysis. Genetic operators like cross over rate, mutation rate can be defined by the user. Experiments & observations were recorded w.r.t variable parameters like fixed popul...

This paper analyzes a recombination/mutation/selection genetic algorithm that uses gene pool recombination. For linear fitness functions, the infinite population model can be described by ` equations where ` is the string length. For linear fitness functions, we show that there is a single fixed point and that this fixed point is stable. For the ONEMAX fitness function, the model reduces to a l...

Due to experimental evidence it is incontestable that crossover is essential for some fitness functions. However, theoretical results without assumptions are difficult. So-called real royal road functions are known where crossover is proved to be essential, i. e., mutation-based algorithms have an exponential expected runtime while the expected runtime of a genetic algorithm is polynomially bou...

Many optical or image processing tasks reduce to the optimization of some set of parameters. Genetic algorithms can optimize these parameters even when the functions they map are fairly complicated, but they can only do so the point where the fitness functions they are given can differentiate between good results and the best result. This can occur when the optimal point is in a region (in a th...

In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized usin...

A new modification to the genetic algorithm is presented which is specifically designed to increase the rate of evolution on fitness functions with high degrees of neutrality (mutations that do not change the individual’s fitness). Instead of allowing random genetic drift to occur when most of the population has reached the same fitness, the “reproduction fitness” of individuals is set to their...

Automatic path oriented test data generation is not only a crucial problem but also a hot issue in the research area of software testing today. In this paper genetic algorithm (GA) has been used as a robust metaheuristic search method under basis path testing coverage criteria. Two types of fitness function have been used, one is branch distance based fitness function (BDBFF) and other is appro...

In this paper we present a version of genetic algorithm GA where parameters are created by the GA, rather than predetermined by the programmer. Chromosome portions which do not translate into fitness “genetic residual” are given function to diversify control parameters for the GA, providing random parameter setting along the way, and doing away with fine-tuning of probabilities of crossover and...