Evolutionary Art

Evolutionary Art exploits the process of evolution to create an artwork which continually changes according to an evolutionary algorithm. In common with natural selection and animal husbandry, the members of a population undergoing artificial evolution modify their form or behaviour over many reproductive generations in response to a selective regime. In Interactive Evolution the selective regime may be applied by the viewer explicitly by selecting individuals which are aesthtically pleasing, as in Richard Dawkins' Biomorphs program. Alternatively a selection pressure can be generated implicitly, for example according to the length of time a viewer spends near a piece of evolving art. Equally, evolution may be employed as a mechanism for generating a dynamic world of adaptive individuals, in which the selection pressure is imposed by the program, and the viewer plays no role in selection, as in the Black Shoals project.

Evolution Algorithm

An evolutionary algorithm (also EA, evolutionary computation, artificial evolution) is a generic term used to indicate any population-based optimization algorithm that uses mechanisms inspired by biological evolution, such as reproduction, mutation and recombination (see genetic operators). Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solutions "live" (see also fitness function). Evolution of the population then takes place after the repeated application of the above operators. Specific examples of EAs are given below. Most of these techniques are similar in spirit, but differ in the details of their implementation and the nature of the particular problem to which they have been applied. Because they do not make any assumption about the underlying fitness landscape, it is generally believed that evolutionary algorithms perform consistently well across all types of problems (see, however, the no-free-lunch theorem). This is evidenced by their success in fields as diverse as engineering, art, biology, economics, genetics, operations research, robotics, social sciences, physics, chemistry, and others. Apart from their use as mathematical optimizers, evolutionary computation and algorithms have also been used as an experimental framework within which to validate theories about biological evolution and natural selection, particularly through work in the field of artificial life. Techniques from evolutionary algorithms applied to the modelling of biological evolution are generally limited to explorations of microevolutionary processes, however some computer simulations, such as Tierra and Avida, attempt to model macroevolutionary dynamics. A limitation of evolutionary algorithms is their lack of a clear genotype-phenotype distinction. In nature, the fertilized egg cell undergoes a complex process known as embryogenesis to become a mature phenotype. This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism. Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns.

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