Overview of genetic algorithms

Overview of genetic algorithms

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Genetic algorithms (GAs) belong to the group of metaheuristic optimization methods. They are based on mimicking the process of evolution in nature. Evolution refers to constant adaptation of living beings to varying conditions in the environment. Individuals with the largest ability to adapt have the best chances to survive. There is an ongoing ruthless fight for survival in nature resulting in the survival of the fittest and perishing of the weakest individuals. In order for a species to survive during evolution, it must adapt to the surrounding conditions and the environment for these constantly changes. Each succeeding generation of any species must retain the good properties of the preceding generation while improving and altering them so that the quality of individuals in the population is continuously enhanced.

Chapter Contents:

  • 2.1 Introduction
  • 2.2 Basic structure of the GA
  • 2.3 Representation of individuals (encoding)
  • 2.3.1 Binary encoding
  • 2.3.2 Gray coding
  • 2.3.3 Real-value encoding
  • 2.4 Population size and initial population
  • 2.5 Fitness function
  • 2.5.1 Relative fitness
  • 2.5.2 Linear scaling
  • 2.6 Selection
  • 2.6.1 Simple selection
  • 2.6.2 Stochastic universal sampling
  • 2.6.3 Linear ranking selection
  • 2.6.4 Elitist selection
  • 2.6.5 k-Tournament selection schemes
  • 2.6.6 Simple tournament selection
  • 2.7 Crossover
  • 2.7.1 One-point crossover
  • 2.7.2 Multipoint crossover
  • 2.7.3 Uniform crossover
  • 2.7.4 Shuffle crossover
  • 2.7.5 Arithmetic crossover
  • 2.7.6 Heuristic crossover
  • 2.8 Mutation
  • 2.9 GA control parameters
  • 2.10 Multiobjective optimization using GA
  • 2.11 Applications of GA to power system problems—literature overview
  • 2.11.1 Optimal power flow
  • 2.11.2 Optimal reactive power dispatch
  • 2.11.3 Combined economic and emission dispatch
  • 2.11.4 Optimal power flow in distribution networks
  • 2.11.5 Optimal placement and sizing of distributed generation in distribution networks
  • 2.11.6 Optimal energy and operation management of microgrids
  • 2.11.7 Optimal coordination of directional overcurrent relays
  • 2.11.8 Steady-state analysis of self-excited induction generator
  • 2.12 Conclusion
  • References

Inspec keywords: genetic algorithms

Other keywords: metaheuristic optimization methods; genetic algorithms

Subjects: Optimisation techniques; Optimisation; Optimisation techniques

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