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Machine Learning in Evolutionary Computation

Evolutionary computation in machine learning is mainly focused on solving optimization problems that fail by using classical numerical methods with poor results. Evolutionary computation offers a reliable and effective approach to address complex problems in real-world applications in wider application domains such as agriculture, manufacturing, power and energy, networking, finance, and healthcare. Evolutionary computation(EC) is a subfield of computer intelligence, and an optimization methodology refers to the mechanisms of biological evolution and behavior of living organisms. It models the essential elements of biological evolution and explores the solution space by gene inheritance and mutation. Evolutionary approaches in machine learning involve prepossessing, learning, and post-processing. Enhanced-EC algorithm (MLEC) stores sample data about the problem features, search space, and population information during the iterative search process. Thus the machine learning technique helps analyze these data for enhancing the search performance. EC algorithms include genetic algorithm (GA), evolutionary programming (EP), evolutionary strategies (ES), genetic programming (GP), learning classifier systems (LCS), differential evolution (DE), estimation of distribution algorithm(EDA), and memetic algorithms. Future scopes of Evolutionary computation are Combining modular evolutionary machine learning, Transfer learning using machine learning models, and Multi/many-objective evolutionary machine learning.