Abstract

Traditional methods often employed to solve complex real world problems tend to inhibit elaborate exploration of the search space. They can be expensive and often results in sub-optimal solutions. Evolutionary computation (EC) is generating considerable interest for solving real world engineering problems. They are proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. EC harnesses the power of natural selection to turn computers into optimisation tools. The core methodologies of EC are genetic algorithms (GA), evolutionary programming (EP), evolution strategies (ES) and genetic programming (GP). This paper attempts to bridge the gap between theory and practice by exploring characteristics of real world problems and by surveying recent EC applications for solving real world problems in the manufacturing industry. The survey outlines the current status and trends of EC applications in manufacturing industry. For each application domain, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of inhibitors to industrial applications of optimisation algorithms.

Keywords: Evolutionary computing; Genetic algorithms; Manufacturing industry

Article Outline

1. Introduction

2. Algorithmic approaches to optimisation

3. Evolutionary computation

3.1. Genetic algorithms

3.2. Genetic programming

3.3. Evolutionary programming

3.4. Evolutionary strategies

4. Characteristics of real world problems

4.1. Number of variables and the ‘curse of dimensionality’

4.2. Complex search space

4.3. Adaptability

4.4. Hierarchical

4.5. Human interaction

5. Evolutionary computation in industry

5.1. Metal forming industry

5.2. Paper industry

5.3. Scheduling and process planning problems in production industry

5.4. Chemical industry

5.5. Curve/surface optimisation

5.6. Related manufacturing applications

6. Inhibitors to industrial applications of EC-based optimisation algorithms

7. Concluding remarks

References