Gradient descent techniques such as back propagation have been used effectively to train neural network connection weights; however, in some applications gradient information may not be available. Biologically inspired genetic algorithms provide an alternative. Unfortunately, early attempts to use genetic algorithms to train connection weights demonstrated that exchanging genetic material betwe...

Real-coded genet ic algorit hms (GAs) do not use any coding of the problem variab les, instead they work dir ectly with the variab les . The main difference in the implementation of real-coded GAs and binary-coded GAs is in their recombination op erators. Alt ho ugh a number of real-cod ed crossover implementations were suggested, most of them were developed wit h intuition and wit hout much an...

Genetic algorithms are optimisation algorithms and mimic the natural process of evolution. Important operators used in genetic algorithms are selection, crossover and mutation. Selection operator is used to select the individuals from a population to create a mating pool which will participate in reproduction process. Crossover and mutation operators are used to introduce diversity in the popul...

The concept of self-organization is applied to the operators and parameters of genetic algorithm to develop a novel Auto-poietic algorithm solving a biological problem, Multiple Sequence Alignment (MSA). The self-organizing crossover operator of the developed algorithm undergoes a swap and shuffle process to alter the genes of chromosomes in order to produce better combinations. Unlike Standard...

This paper analyzes the performance of a genetic algorithm that utilizes tournament selection, one-point crossover, and a reordering operator. A model is proposed to describe the combined effect of the reordering operator and tournament selection, and the numerical solutions are presented as well. Pairwise, s-ary, and probabilistic tournament selection are all included in the proposed model. It...

Recombination operators with high positional bias are less disruptive against adjacent genes. Therefore, it is ideal for the encoding to position epistatic genes adjacent to each other and aid GA search through genetic linkage. To produce an encoding that facilitates genetic linkage is problematic. This study focuses on selective crossover, which is an adaptive recombination operator. We propos...

Recombination operators with high positional bias are less disruptive against adjacent genes. Therefore, it is ideal for the encoding to position epistatic genes adjacent to each other and aid GA search through genetic linkage. To produce an encoding that facilitates genetic linkage is problematic. This study focuses on selective crossover, which is an adaptive recombination operator. We propos...

Genetic algorithms (GAs) generate solutions to optimization problems using techniques inspired by natural evolution, like crossover, selection and mutation. In that process, crossover operator plays an important role as an analogue to reproduction in biological sense. During the last decades, a number of different crossover operators have been successfully designed. However, systematic comparis...

Multi-objective problems with parameter interactions can present difficulties to many optimization algorithms. We have investigated the behaviour of Simplex Crossover (SPX), Unimodal Normally Distributed Crossover (UNDX), Parent-centric Crossover (PCX), and Differential Evolution (DE), as possible alternatives to the Simulated Binary Crossover (SBX) operator within the NSGA-II (Non-dominated So...

The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness” function. In that process, crossover operator plays an important role. To comprehend the GAs as a whole, it is necessary to understand ...