Genetic algorithms (GAs) belong to the broader family of generic population based
metaheuristic optimisation algorithms called evolutionary algorithms. These are one of the …

Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are
used in learning and modeling especially with the advent of big data related problems. This …

In this paper, evolutionary algorithms (EAs) are deployed for multi-objective Pareto optimal
design of group method of data handling (GMDH)-type neural networks which have been …

This research article presents an integrated approach to optimization of wire electrical
discharge machining (WEDM) of gamma titanium aluminide (γ-TiAl) with the assistance of …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …

Flank wear occurring on the cutting edge increases as machining time goes on. This results
in an increase in the cutting force and the surface roughness which leads to product quality …

In this paper, a multi-objective 2-dimensional vector packing problem is presented. It
consists in packing a set of items, each having two sizes in two independent dimensions …

A new genetic algorithms based multi-objective optimization algorithm (NMGA) has been
developed during study. It works on a neighborhood concept in the functional space, utilizes …

The work presented here describes a method of optimizing the layout of rectangular parts
placed on a rectangular sheet to cut out various parts. Two types of cutting problems have …

In this work, genetic algorithm (GA) technique was applied to investigate cutting process
parameters influence on workpiece price formation using the concepts of contribution …