Practical Genetic Algorithms

  title={Practical Genetic Algorithms},
  author={Randy L. Haupt and Sue Ellen Haupt},
Introduction to Optimization The Binary Genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index. 
Self-configuring genetic programming algorithm with modified uniform crossover
For genetic programming algorithms new variants of uniform crossover operators that introduce selective pressure on the recombination stage are proposed. Operators probabilistic rates based approach
Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator
For genetic algorithms, new variants of the uniform crossover operator that introduce selective pressure on the recombination stage are proposed. Operator probabilistic rates based approach to
Using genetic algorithms in software optimization
Genetic algorithms are proposed for multi-criteria optimization for very complex software applications and their effects are compared with classic algorithm effects for the same lot of applications.
Truss Optimization Using Genetic Algorithms
The design of a program that uses genetic algorithms to optimize a truss structure, along with an example of truss optimization, are presented.
Application of genetic algorithms to construction scheduling with or without resource constraints
The difficulties encountered in scheduling construction projects with resource constraints are highlighted by means of a simplified bridge construction problem. A genetic algorithm applicable to pr...
A Hybrid Fuzzy Simplex Genetic Algorithm
This paper presents a novel hybrid genetic algorithm that has the ability of the genetic algorithms to avoid being trapped at local minimum while accelerating the speed of local search by using the
DNA coding in evolutionary computation
This work compares two coding methods for the genetic algorithm and the effect of the developed coding methods on the algorithms is analyzed by application to classification problem.
Efficiency Indicators of Certain Parallel Population-Based Optimization Algorithms
A comparative analysis of efficiency indicators of parallel population-based optimization algorithms – genetic, ant colony, and particle swarm optimization of McCormick, Rastrigin, Beale, and Booth is presented.
A concept for the optimal planning of serial production by means of a genetic algorithm and its application by way of a computer program is presented, ensuring fast business decision making and thereby enhancing the competitiveness of the enterprise.


The Nature of Mutation in Genetic Algorithms
The variables of numerical functions are treated as the genes, and the role of mutation as an independent reproduction operator in Genetic Algorithms is investigated.
Stopping Criteria for Finite Length Genetic Algorithms
Borders are derived on the number of iterations required to achieve a level of confidence to guarantee that a genetic algorithm has seen all populations and, hence, an optimal solution.
Genetic Algorithms in Search Optimization and Machine Learning
This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Artificial Intelligence through Simulated Evolution
This chapter contains sections titled: References Artificial Intelligence through a Simulation of Evolution Natural Automata and Prosthetic Devices and Artificial intelligence through a simulation of Evolution natural automata and prosthetic devices.
Parallel genetic algorithm taxonomy
  • M. Nowostawski, R. Poli
  • Computer Science
    1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410)
  • 1999
The state of the art on PGAs is reviewed and a new taxonomy also including a new form of PGA (the dynamic deme model) which was recently developed is proposed.
Evolution Strategies: An Alternative Evolutionary Algorithm
It is argued that the application of canonical genetic algorithms for continuous parameter optimization problems implies some difficulties caused by the encoding of continuous object variables by binary strings and the constant mutation rate used in genetic algorithms.
Genetic Programming III: Darwinian Invention & Problem Solving
Genetically evolved solutions to dozens of problems of design, control, classification, system identification, and computational molecular biology are presented.
Convergence Criteria for Genetic Algorithms
It is shown that by running the genetic algorithm for a sufficiently long time the authors can guarantee convergence to a global optimum with any specified level of confidence, and an upper bound for the number of iterations necessary to ensure this is obtained.
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
The strengths of the proposed deterministic schedule and the self-adaptation method are demonstrated by a comparison of their performance on difficult combinatorial optimization problems, and both methods are shown to perform significantly better than the canonical genetic algorithm.