Cycle crossover (CX) combines two permutation parents by identifying position-linked cycles, preserving selected cycles from each parent, and filling the remaining positions from the other parent. section 8.3.1
Cycle crossover
A permutation crossover operator that partitions aligned parent elements into cycles and combines those cycles to form feasible offspring.
How Optimization Algorithms: AI techniques for design, planning, and control problems covers it
- How deep it goes
- Explains cycle discovery and offspring filling procedurally, supported by a dedicated algorithm and figure.
- Examples
- Uses a dedicated cycle-crossover figure to illustrate cycle identification and offspring construction.
- What it assumes
- Assumes familiarity with parent chromosomes, offspring, aligned positions, and permutation-based genetic algorithms.
- What you can do after
- Apply CX to two permutation parents and produce two offspring while preserving parent-position cycles and avoiding duplicate elements.
- How it connects
- Connects permutation-based genetic algorithms with PMX, edge crossover, order 1 crossover, and the traveling salesman problem.
Related concepts
- crossoverkind of · Crossover is an evolutionary recombination technique that combines material from parent candidate solutions to produce one or more offspring.
- order crossoveralternative to · Order crossover is a permutation-preserving genetic operator that combines parent sequences while retaining each required element exactly once and preserving selected relative order.
This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.