Evolutionary algorithms: A critical review and its future prospects

@article{Vikhar2016EvolutionaryAA,
  title={Evolutionary algorithms: A critical review and its future prospects},
  author={P. A. Vikhar},
  journal={2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)},
  year={2016},
  pages={261-265}
}
  • P. Vikhar
  • Published 1 December 2016
  • Computer Science
  • 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)
Evolutionary algorithm (EA) emerges as an important optimization and search technique in the last decade. [] Key Method It also includes unusual study of various invariants of EA like Genetic Programming (GP), Genetic Algorithm (GA), Evolutionary Programming (EP) and Evolution Strategies (ES). Extensions of EAs in the form of Memetic algorithms (MA) and distributed EA are also discussed. Further the paper focuses on various refinements done in area of EA to solve real life problems.

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References

SHOWING 1-10 OF 20 REFERENCES
Introduction to evolutionary algorithms
  • Xinjie Yu
  • Computer Science
    The 40th International Conference on Computers & Indutrial Engineering
  • 2010
Some interesting features of the new book “Introduction to Evolutionary Algorithms”, which is written by Xinjie Yu and Mitsuo Gen and be published by Springer in 2010, will be illustrated, including
Evolutionary programming made faster
TLDR
A "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator and is proposed and tested empirically, showing that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
Experimental optimization by evolutionary algorithms
This tutorial addresses applications of evolutionary algorithms to optimization tasks where the function evaluation cannot be done through a computer simulation, but requires the execution of an
Evolutionary Algorithm Definition
TLDR
This study discussed the use of new evolutionary algorithm for automatic programming, based on theoretical definitions of program behaviors, which enhanced evolutionary process by simultaneously solving multi-parts from the same problem.
Comparison of multiobjective particle swarm optimization and evolutionary algorithms for optimal reactive power dispatch problem
TLDR
The comparison results indicate that MOPSO generally outperforms other algorithms for ORPD and has a great potential in dealing with large-scale optimal power flow problems.
A theoretical assessment of solution quality in evolutionary algorithms for the knapsack problem
TLDR
A theoretical investigation of three types of (N+1) evolutionary algorithms that exploit bitwise mutation, truncation selection, plus different repair methods for the 0-1 knapsack problem indicates that the solution produced by both pure strategy and mixed strategy evolutionary algorithms is arbitrarily bad.
A Primary Theoretical Study on Decomposition-Based Multiobjective Evolutionary Algorithms
TLDR
The studies show that in certain cases, polynomialsized evenly distributed weight parameters-based decomposition cannot map each point in a polynomial sized Pareto front to a subproblem; an ideal serialized algorithm can be very efficient on some simple instances; the standard MOEA can benefit a runtime cut of a constant fraction from its neighborhood coevolution scheme.
Improved differential evolution algorithms
TLDR
Two modified differential evolution are introduced by merging the mechanisms of quadratic approximation, Gaussian disturbing, immune theory and differential evolution to improve the searching ability of differential evolution.
An overview of evolutionary algorithms: practical issues and common pitfalls
Parallel Evolutionary Algorithms
  • Dirk Sudholt
  • Computer Science
    Handbook of Computational Intelligence
  • 2015
TLDR
This paper focuses on island models, or coarse-grained EA s, which have shown that island models can speed up computation significantly, and that parallel populations can further increase solution diversity.
...
...