Neutral network and machine learning Research Paper

Neutral network and machine learning - Research Paper Example Problems used to be in form of binary strings of 0s and 1s. Currently, there is usage of other encodings. This evolution normally begins from a group of randomly created phenotypes and this process takes place through generations. During each generation, the fitness of each individual in the population/group is cross examined, multiple phenotypes are chosen from the group as per their fitness and then they are modified and can be randomly mutated to create a new population which is then used in the iteration calculations whose procedure is step-by-step also known as the algorithm. This algorithm is mostly terminated after the production of a maximum number of generations. A fulfilling solution may or may not be accomplished if the algorithm has been terminated when because of a maximum number of generations. The most widely accepted representation of the result is using an array of bits. Any other arrays can be used similarly. What makes the representation that uses genetics convenie nt is the fact that their parts can be aligned conveniently because of their fixed size. This facilitates easy crossover operations. 1.2 Applications and results of Genetic Algorithm 1.2.1Metaheuristic This term is designated from a computational method which optimizes problems through iteration. This iteration tries to improve the solution of a candidate as per a given measure of quality. Few or no assumptions are made about the problem being optimized. As far as candidate solutions are involved it can search very large spaces. However, optimal solutions are not guaranteed to be found by Metaheuristic. Stochastic optimization is mostly implemented in a metaheuristic way. It can also be referred to as: Derivative free Direct search Black box Heuristic optimizer 1.2.2 Computational creativity This is also referred to as artificial, mechanical creativity and sometimes creative computation. It comprises of the bringing together of fields such as cognitive psychology, artificial intelli gence and philosophy. Computational creativity improvises the combinational perspective which allows one to model creativity in form of a search procedure through several possible combinations. These combinations can be as a result of composition of different representations. Cross over representations which capture different inputs can be generated using neural networks and genetic algorithms. 1.2.3 Multiple sequence alignment This refers to a sequence alignment of at least 3 biological sequences namely: Protein Dna Rna Most of the times the sequences are assumed to have an evolutionary relationship through which they are descended from a common ancestor hence share a lineage. As a result, sequence homology can be inferred from the Multiple Sequence Alignment and to look into the sequences’ shared evolutionary origins phylogenetic analysis is carried out. In trying to widely simulate the evolutionary process which gave rise to the broadening of the query set, genetic algorit hms have been used for production of Multiple Sequence Alignment.This is done by breaking several potential MSAs into pieces and rearranging the pieces repeatedly.Gaps are introduced at several positions.During simulation a common objective function is achieved which is the sum-of-pairs function that emerges in the broad programming Multiple sequence alignment. 1.3 GA (genetic algorithm) used with NN (neural networks) 1.3.1 Evolving weights The frequent use of GA with NN is because genetic algorithms