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ABSTRACT

This paper presents a novel evolutionary approach to solve numerical optimization problems, called Adaptive Evolution (AEv). AEv is a new micro-population-like technique because it uses small populations (less than 10 individuals). The two main mechanisms of AEv are elitism and adaptive behavior. It has an adaptive parameter to adjust the balance between global exploration, local exploitation and elitism. Its two crossover operators allow a newly-generated offspring to be parent of other offspring in the same generation. AEv requires the fine-tuning of two parameters (several state-of-the-art approaches use at least three). AEv is tested on a set of 10 benchmark functions with 30 decision variables and it is compared with respect to some state-of-the-art algorithms to show its competitive performance.

REFERENCES

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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Unconstrained optimization

General Terms:
Algorithms, Experimentation, Performance

Keywords:
evolutionary algorithms, meta-heuristics, numerical optimization