Suite of Evolutionary Optimization Algorithms

Version 1.0.4 (1.3 MB) by EvoLab
A suite of different evolutionary optimization algorithms
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Updated 2 Dec 2025

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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
  • evolutionary strategy
  • big bang-big crunch algorithm
and a set of benchmark problems to test (i.e., CEC 2008, 2013).
Run the 'optimizeProblem.mat' file. Select your settings from this file.
Developed by F. Stroppa.

Cite As

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

MATLAB Release Compatibility
Created with R2025b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
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