Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives

Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA)

“…They stressed the evolution of new knowledge and materials through soft computing activities, which could help structural metals solve correlation problems. Some recent studies have proposed a number of evolutionary approaches for data-driven modeling, Giri et al [24] used the bi-objective genetic programming (BioGP) technique, which initially minimizes training errors through a single objective optimization procedure, and then tradesoff between complexity and accuracy, and is determined OPTIMIZED OPTICAL BRIGHTNESS PARAMETER 3 through a GA-based bi-objective optimization strategy. The early developed metamodels were constructed for a simulated moving bed (reactors were compared with those obtained from an evolutionary neural network or EvoNN), with a polynomial regression model.…”

A novel approach for estimation of solvent activity in polymer solutions using genetic programming

“…Genetic programming (GP), offered by Koza [41,42] as a method to genetically develop populations of mathematical models for prediction of a system behavior with even high complexity. GP, which is based on the bio-inspired technique, is defined as automatically defined function; that is able to automatically discover a computer programming that is well predict a system or problem [35,43].…”

Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

“…Such meta-models need to be sufficiently accurate and should not inherit random noise from the original data source. In recent years newer algorithms have been proposed 7,42,43 that can efficiently use the multi-objective genetic algorithms themselves to come up with metamodels of right accuracy and complexity, which are increasingly being used in engineering studies. [42][43][44] Such meta-models enabled prediction of newer steels with superior and optimised properties, which were also verified experimentally.…”

“…In recent years newer algorithms have been proposed 7,42,43 that can efficiently use the multi-objective genetic algorithms themselves to come up with metamodels of right accuracy and complexity, which are increasingly being used in engineering studies. [42][43][44] Such meta-models enabled prediction of newer steels with superior and optimised properties, which were also verified experimentally. 22 It made it possible accurately to predict the yield point in steel, through calculation of the Hill's coefficients, 45 without making customary ad hoc assumptions regarding the strain offset level.…”