Today, in this world of information processing, business intelligence and business analytics, are gaining in importance very fast. The three major techniques or groups of algorithms which have gained a lot of visibility in recent times are fuzzy logic, neural networks and genetic algorithms. In this article, we discuss in brief about the possible business applications of genetic algorithms.
In general, genetic algorithms find their application in problem domains that have a complex fitness landscape with many criteria with divergent needs or fitness function curves. Typically, genetic algorithms find application in computational science, engineering, manufacturing, phylogenetics, bioinformatics, economics, chemistry, mathematics, physics and other related disciplines.
The most popular application of Genetic Algorithms is in the field of Search applications.
Mostly, in such search techniques, the end objective may be an optimization function where the genetic algorithm may be applied for Fast Search through a huge pool of possible solutions so as to find the most optimal solution fast, without compromising the fitness of purpose too much.
Similarly, problems which are often solved using genetic algorithms include time-table scheduling and job-scheduling problems. Many scheduling software packages use Genetic Algorithms as a predictor model. GAs have also been applied to classic engineering disciplines besides the information technology, communications, telecommunications, electronics and semiconductor industry. Genetic algorithms are also popular for usage as an approach to solve global optimization problems.
Some of the other popular applications of Genetic Algorithms are in computer-automated design, artificial creativity, automated design of industrial equipment, design of mechatronic systems, in the financial services sector, design of multi-objective decision making problems, bioinformatics, quality control, operations planning, chemical kinetics, clustering, code decryption, configuration applications, design of distribution systems, control engineering, computer network design, gene profiling, electronic circuit design, economics modelling, scheduling applications, marketing mix strategizing, molecular research, neural network based applications, software engineering, data compression, plant floor layout planning, and so much more.
By the way, did you read our article on the application of Fuzzy logic and Fuzzy set theory.