GeneticSharp.Domain.Crossovers Namespace

A base class for crossovers.

Exception throw when an error occurs during the execution of cross.

Crossover service.

Cut and Splice crossover. Results in a change in length of the children strings. The reason for this difference is that each parent string has a separate choice of crossover point. Wikipedia

Cycle Crossover (CX). The Cycle Crossover (CX) proposed by Oliver builds offspring in such a way that each city (and its position) comes from one of the parents. A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem The Cycle Crossover operator identifies a number of so-called cycles between two parent chromosomes. Then, to form Child 1, cycle one is copied from parent 1, cycle 2 from parent 2, cycle 3 from parent 1, and so on. Crossover Technique: Cycle Crossover

One-Point crossover. A single crossover point on both parents is selected. All data beyond that point in either is swapped between the two parents. One-point crossover Parents: |0|0|0| x |1|1|1| Have two swap points indexes: 0 and 1. 1) new OnePointCrossover(0); Children result: |0|1|1| and |1|0|0| 2) new OnePointCrossover(1); Children result: |0|0|1| and |1|1|0|

Ordered Crossover (OX1). Also know as: Order Crossover. A portion of one parent is mapped to a portion of the other parent. From the replaced portion on, the rest is filled up by the remaining genes, where already present genes are omitted and the order is preserved. Crossover for Ordered Chromosomes The Ordered Crossover method is presented by Goldberg, is used when the problem is of order based, for example in Ushaped assembly line balancing etc. Given two parent chromosomes, two random crossover points are selected partitioning them into a left, middle and right portion. The ordered two-point crossover behaves in the following way: child1 inherits its left and right section from parent1, and its middle section is determined. A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem The order crossover operator (Figure 4) was proposed by Davis (1985). The OX1 exploits a property of the path representation, that the order of cities (not their positions) are important. Genetic Algorithms for the Travelling Salesman Problem - A Review of Representations and Operators Order 1 Crossover is a fairly simple permutation crossover. Basically, a swath of consecutive alleles from parent 1 drops down, and remaining values are placed in the child in the order which they appear in parent 2. Order 1 Crossover

Partially mapped crossover (PMX). The partially-mapped crossover operator was suggested by Gold- berg and Lingle (1985). It passes on ordering and value information from the parent tours to the offspring tours. A portion of one parent’s string is mapped onto a portion of the other parent’s string and the remaining information is exchanged. Genetic Algorithms for the Travelling Salesman Problem - A Review of Representations and Operators

Three Parent Crossover. In this technique, the child is derived from three parents. They are randomly chosen. Each bit of first parent is checked with bit of second parent whether they are same. If same then the bit is taken for the offspring otherwise the bit from the third parent is taken for the offspring. Wikipedia

Two-Point Crossover Two-point crossover calls for two points to be selected on the parents. Everything between the two points is swapped between the parents, rendering two children. Wikipedia

The Uniform Crossover uses a fixed mixing ratio between two parents. Unlike one-point and two-point crossover, the Uniform Crossover enables the parent chromosomes to contribute the gene level rather than the segment level. If the mix probability is 0.5, the offspring has approximately half of the genes from first parent and the other half from second parent, although cross over points can be randomly chosen. Wikipedia

Defines a interface for a crossover genetic operator. In genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. It is analogous to reproduction and biological crossover, upon which genetic algorithms are based. Cross over is a process of taking more than one parent solutions and producing a child solution from them. Crossover (Genetic Algorithm)