Genetic representation

In computer programming, genetic representation is a way of representing solutions/individuals in evolutionary computation methods. Genetic representation can encode appearance, behavior, physical qualities of individuals. Designing a good genetic representation that is expressive and evolvable is a hard problem in evolutionary computation. Difference in genetic representations is one of the major criteria drawing a line between known classes of evolutionary computation.

Terminology often comes by analogy with natural genetics. The block of computer memory that represents one candidate solution is called an individual. The data in that block is called a chromosome. Each chromosome consists of genes. The possible values of a particular gene are called alleles. A programmer may represent all the individuals of a population using binary encoding, permutational encoding, encoding by tree, or any one of several other representations.^[1]

Genetic algorithms use linear binary representations. The most standard one is an array of bits. Arrays of other types and structures can be used in essentially the same way. The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size. This facilitates simple crossover operation. Variable length representations were also explored in Genetic algorithms, but crossover implementation is more complex in this case.

Evolution strategy uses linear real-valued representations, e.g. an array of real values. It uses mostly gaussian mutation and blending/averaging crossover.

Genetic programming (GP) pioneered tree-like representations and developed genetic operators suitable for such representations. Tree-like representations are used in GP to represent and evolve functional programs with desired properties.^[2]

Human-based genetic algorithm (HBGA) offers a way to avoid solving hard representation problems by outsourcing all genetic operators to outside agents, in this case, humans. The algorithm has no need for knowledge of a particular fixed genetic representation as long as there are enough external agents capable of handling those representations, allowing for free-form and evolving genetic representations.

Common genetic representations

• binary array
• binary tree
• natural language
• parse tree
• directed graph

References and notes