Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Quality Engineering Using Robust Design
Quality Engineering Using Robust Design
Mechanical Component Design for Multiple Objectives Using Elitist Non-dominated Sorting GA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Variable Dependence Interaction And Multi-objective Optimisation
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An empirical study of evolutionary techniques for multiobjective optimization in engineering design
An empirical study of evolutionary techniques for multiobjective optimization in engineering design
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
An orthogonal multi-objective evolutionary algorithm with lower-dimensional crossover
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An efficient multi-objective evolutionary algorithm: OMOEA-II
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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Interaction among decision variables is inherent to a number of real-life engineering design optimisation problems. There are two types of variable interaction: inseparable function interaction and variable dependence. The aim of this paper is to present an Evolutionary Computing (EC) technique for handling complex inseparable function interaction, and to demonstrate its effectiveness using three case studies. The paper begins by devising a definition of inseparable function interaction, identifying the challenges and presenting a review of relevant literature. It then briefly describes Generalised Regression GA (GRGA) for handling complex inseparable function interaction in multi-objective optimisation problems. GRGA is applied to a complex test problem and two real-life engineering design optimisation case studies that exhibit complex inseparable function interaction. It is shown that GRGA exhibits better convergence and distribution of solutions than NSGA-II, which is a high-performing evolutionary-based multi-objective optimisation algorithm. The paper concludes by presenting the future research directions.