Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used in binary-coded GAs. How...

In many industries, major projects are performed in groups. Keeping this in mind, many academic courses are fitted with syllabus that boosts the students’ group learning and group working skills. Unfortunately, optimal group formation among students is a challenging task. Genetic algorithm can be applied to solve this problem which is evident from various literatures. Genetic algorithms are pur...

Crossovers between meiotic homologs are crucial for their proper segregation, and crossover number and position are carefully controlled. Crossover homeostasis in budding yeast maintains crossovers at the expense of noncrossovers when double-strand DNA break (DSB) frequency is reduced. The mechanism of maintaining constant crossover levels in other species has been unknown. Here we investigate ...

Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques which combine multiple crossovers have been su...

Geometric crossover is a representation-independent generalization of the traditional crossover defined using the distance of the solution space. By choosing a distance firmly rooted in the syntax of the solution representation as a basis for geometric crossover, one can design new crossovers for any representation. Using a distance tailored to the problem at hand, the formal definition of geom...

Evolutionary algorithms (EAs), simulating the evolution process of natural species, are used to solve optimization problems. Crossover (also called recombination), originated from simulating the chromosomeexchange phenomena in zoogamy reproduction, is widely employed in EAs to generate offspring solutions, of which the effectiveness has been examined empirically in applications. However, due to...

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesm...

We study crossover phenomena in a model of self-avoiding walks with medium-range jumps, which corresponds to the limit N-->0 of an N-vector spin system with medium-range interactions. In particular, we consider the critical crossover limit that interpolates between the Gaussian and the Wilson-Fisher fixed point. The corresponding crossover functions are computed by using field-theoretical metho...

The homotypic fusion of endoplasmic reticulum membranes is catalyzed by the atlastin GTPase. The mechanism involves trans-dimerization between GTPase heads and a favorable crossover conformational shift, catalyzed by GTP hydrolysis, that converts the dimer from a "prefusion" to "postfusion" state. However, whether crossover formation actually energizes fusion remains unclear, as do the sequence...