Suite of Evolutionary Optimization Algorithms

A suite of different evolutionary optimization algorithms

https://evo.khas.edu.tr

버전 1.1.3 (1.31 MB)
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2026/7/7

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MATLAB implementation of the different evolutionary algorithms for -- currently only -- single unconstraint global optimization.
The library contains:
  • generational genetic algorithm
  • steady-state genetic algorithm
  • evolution strategy (1/5 rule and self-adaptation for mutation)
  • big bang-big crunch algorithm
and a set of benchmark problems to test:
  • CEC 2008
  • CEC 2013
  • BBOB 2009 (COCO-style with instance generator)
Run the 'optimizeProblem.mat' file. Select your settings from this file.
Developed by F. Stroppa.

인용 양식

EvoLab (2026). Suite of Evolutionary Optimization Algorithms (https://kr.mathworks.com/matlabcentral/fileexchange/182675-suite-of-evolutionary-optimization-algorithms), MATLAB Central File Exchange. 검색 날짜: .

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버전 퍼블리시됨 릴리스 정보
1.1.3

merged Evolution Strategy adaptations (one-fifth and self-adaptation)
1.1.2

fixed a bug in the size of the convergence array (result of division is now integer)
1.1.1

- included version with 1/5 rule for mutation in ES
- fixed a bug on learning rates for self-adaptive ES
- optimized some parameters
1.1.0

- Included a termination condition on a target of precision = 1e-8
- Convergence plot is now over function evaluations and on error if the true optimum is known, on best fit otherwise
- Convergence plot can be visualized in Log10
1.0.7

Optimized the convergence array to save space.
1.0.6

Added a proper visualizer for multi-dimensional optimization problems (i.e., dim > 3).
1.0.5

Included COCO-style Black-Box Optimization Benchmark problems (BBOB 2009) with instance generator.
1.0.4

Included elitist and non elitist version of BBBC
1.0.3

Included CEC 2013 for large-scale global optimization (problems can scale from 1d to 1000d). Fixed a small bug on BBBC.
1.0.2

generalized the error calculation between true optimum and best retrieved solution in case of multimodal functions
1.0.1

Included a convergence plot, (µ+λ) evolutionary strategy, indices return for every survival stage
1.0.0