In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One-point crossover presents very interesting properties from the theory po...

This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the Collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and th...

A new model for evolving crossover operators for evolutionary function optimization is proposed in this paper. The model is a hybrid technique that combines a Genetic Programming (GP) algorithm and a Genetic Algorithm (GA). Each GP chromosome is a tree encoding a crossover operator used for function optimization. The evolved crossover is embedded into a standard Genetic Algorithm which is used ...

The segregation of homologous chromosomes during the Meiosis I division requires an obligate crossover per homolog pair (crossover assurance). In Saccharomyces cerevisiae and mammals, Msh4 and Msh5 proteins stabilize Holliday junctions and its progenitors to facilitate crossing over. S. cerevisiae msh4/5 hypomorphs that reduce crossover levels up to twofold at specific loci on chromosomes VII, ...

the analytical theory of one dimensional, dispersion free miscible displacement of oil by injection gas with nc component has shown that: the mmp is the lowest pressure at which any one of the initial oil, injection gas or crossover key tie lines becomes critical, which means that its length approaches to zero. in this paper, we propose a method for a solving multi component system based on ana...

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 performance of a genetic algorithm (GA) is dependent on many factors: the type of crossover operator, the rate of crossover, the rate of mutation, population size, and the encoding used are just a few examples. Currently, GA practitioners pick and choose GA parameters empirically until they achieve adequate performance for a given problem. In this paper we have isolated one such parameter: ...

Crossover operator is the predominant operator in most of Genetic Programming (GP) system. The empirical evidence shows that along with building blocks are constructed bigger and bigger as GP evolution proceeds, the crossover operator tends to disrupt those building blocks rather than preserve them. The traditional GP crossover primarily acts as macromutation. Looseness is used for representing...

Premature convergence is one of the important issues while using Genetic Programming for data modeling. It can be avoided by improving population diversity. Intelligent genetic operators can help to improve the population diversity. Crossover is an important operator in Genetic Programming. So, we have analyzed number of intelligent crossover operators and proposed an algorithm with the modific...

Theoretically and empirically it is clear that a genetic algorithm with crossover will outperform a genetic algorithm without crossover in some fitness landscapes, and vice versa in other landscapes. Despite an extensive literature on the subject, and recent proofs of a principled distinction in the abilities of crossover and non-crossover algorithms for a particular theoretical landscape, buil...