Mutation as a Diversity Enhancing Mechanism in Genetic Programming Essay

Mutation as a Diversity Enhancing Mechanism in Genetic Programming - strain ExampleGenetic programming (GP) has emerged as a promising instrument in research on machine learning and artificial intelligence. According to Koza and Poli (2005), GP is a systematic method of getting computers to automatically solve a chore (p. 127). The temptation of creating artificial intelligence and enabling machines to automatically perform problem solving has take to the exploration of biologically inspired methods of programming, such as crossovers and mutations. The process of GP involves alterations in computer programs analogous to biological ancestral processes. The genetic code in biological science is analogous to syntax trees in computer science, and these trees are altered in a similar fashion as that of gene mutation, deletion, crossover, duplication, etc. performed by nature. The aim of genetic programming is to create a novel and manifold program without taking the trouble of pred efining its structure. BackgroundIn the process of biological evolution, organisms underwent alterations in their genetic makeup, which light-emitting diode to an increase in their structural as well as genetic diversity. Only those who were genetically burst were able to survive during the dynamic changes in environmental conditions. Those who lacked the capacity to adapt to these changes went extinct. Thus, according to Charles Darwin, evolution of organisms occurred via earthy selection in which nature selected the organisms that were most fit to survive, also known as survival of the fittest. Mutations are the most effective genetic alterations, which enabled the generation of diversity among organisms and ultimately led to their natural selection in the process of evolution. Mutations occur randomly in the genes, and may be natural or induced. These are abrupt and heritable changes, and occur at a very small frequency. They, however, lead to beneficial or in time harmful ch anges in an organism. Mutation is natures way of generating diversity among living organisms. The fact that random mutations have led to the generation of successful species is enough to inspire the exploration of similar mechanisms in computer science, in a metaphorical sense. With the help of mutations in programming, it may be possible to create novel and successful genetic algorithms or programs with a higher fitness value, which have a high probability of arriving at the solving to a given problem. These may form an integral part of machine learning and help in the synthesis of artificial intelligence. ObjectiveMany studies have explored the role of mutations in genetic programming for the consequence of diversity in computer programs. It is hoped that through such a process, it would be possible to create programs with increase fitness and with more efficient problem solving capacities. This paper attempts at analyzing the importance of diversity in genetic programming and t he efficiency of mutations in achieving the same. The paper, Mutation as a Diversity Enhancing Mechanism in Genetic Programming (Jackson 2011) is also reviewed and evaluated. II.