Overview and software guide of evolutionary algorithms; A case study in quantum control

2003 ◽  
pp. 23-40
Author(s):  
Anne Auger ◽  
Adel Ben Haj-Yedder ◽  
Marc Schoenauer
Keyword(s):  
Evolutionary Algorithms ◽  
Quantum Control

scholarly journals EVOLUTIONARY ALGORITHMS FOR RESOURCE CONSTRAINED NON-SERIAL MIXED FLOW SHOPS

2003 ◽  
Vol 03 (04) ◽  
pp. 411-435 ◽  
Author(s):  
ROBERT L. BURDETT ◽  
ERHAN KOZAN
Keyword(s):  
Evolutionary Algorithms ◽  
Numerical Investigation ◽  
Flow Shop ◽  
Resource Constrained ◽  
Mixed Flow ◽  
Flow Shops ◽  
Constrained Flow

In this paper the resource-constrained flow shop (RCF) problem is addressed. A number of realistic extensions are incorporated, including non-serial precedence requirements, mixed flow shop situations, and the distribution of the human workforce among a number of pre-determined groups. The RCF is then solved by meta-heuristics, primarily of the evolutionary type. An extensive numerical investigation, including a case study of a particular industrial situation, details the implementation and execution of the heuristics, and the efficiency of the proposed algorithms.

Modeling the Performance of Evolutionary Algorithms on the Root Identification Problem: A Case Study with PBIL and CHC Algorithms

2011 ◽  
Vol 19 (1) ◽  
pp. 107-135 ◽  
Author(s):  
Enrique Yeguas ◽  
Robert Joan-Arinyo ◽  
María Victoria Luzón
Keyword(s):  
Evolutionary Algorithms ◽  
Identification Problem ◽  
Parametric Modeling ◽  
Constraint Satisfaction Problems ◽  
Performance Model ◽  
General Constraint ◽  
Search Spaces ◽  
Number Of Iterations

The availability of a model to measure the performance of evolutionary algorithms is very important, especially when these algorithms are applied to solve problems with high computational requirements. That model would compute an index of the quality of the solution reached by the algorithm as a function of run-time. Conversely, if we fix an index of quality for the solution, the model would give the number of iterations to be expected. In this work, we develop a statistical model to describe the performance of PBIL and CHC evolutionary algorithms applied to solve the root identification problem. This problem is basic in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems. The performance model is empirically validated over a benchmark with very large search spaces.

A case study on using evolutionary algorithms to optimize bakery production planning

2013 ◽  
Vol 40 (17) ◽  
pp. 6837-6847 ◽  
Author(s):  
Florian T. Hecker ◽  
Walid B. Hussein ◽  
Olivier Paquet-Durand ◽  
Mohamed A. Hussein ◽  
Thomas Becker
Keyword(s):  
Evolutionary Algorithms ◽  
Production Planning

scholarly journals Evolutionary algorithms for hard quantum control

2014 ◽  
Vol 90 (3) ◽  
Author(s):  
Ehsan Zahedinejad ◽  
Sophie Schirmer ◽  
Barry C. Sanders
Keyword(s):  
Evolutionary Algorithms ◽  
Quantum Control

Multi-Objective Optimization of Manufacturing Processes Using Evolutionary Algorithms

2012 ◽  
pp. 44-66
Author(s):  
M. Kanthababu
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Multiple Objectives ◽  
Manufacturing Processes ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Local Optima ◽  
Multi Objective ◽  
Research Areas

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

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