mcga
3.0.9Machine Coded Genetic Algorithms for Real-Valued Optimization Problems
Overview
Machine coded genetic algorithm (MCGA) is a fast tool for real-valued optimization problems. It uses the byte representation of variables rather than real-values. It performs the classical crossover operations (uniform) on these byte representations. Mutation operator is also similar to classical mutation operator, which is to say, it changes a randomly selected byte value of a chromosome by +1 or -1 with probability 1/2. In MCGAs there is no need for encoding-decoding process and the classical operators are directly applicable on real-values. It is fast and can handle a wide range of a search space with high precision. Using a 256-unary alphabet is the main disadvantage of this algorithm but a moderate size population is convenient for many problems. Package also includes multi_mcga function for multi objective optimization problems. This function sorts the chromosomes using their ranks calculated from the non-dominated sorting algorithm.
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- OK2026-04-2214 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1813 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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- 94%
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- Return-value docs
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- References docs
- 31%
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- RR 4.6.0 released · 2026-04-24
- 3.0.9Latest
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- RR 3.3.0 released · 2016-05-03
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- 3.02016-03-25 · diff ↗
- RR 3.2.0 released · 2015-04-16
- RR 3.1.0 released · 2014-04-10
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- RR 3.0.0 released · 2013-04-03
- 2.0.62012-10-31 · diff ↗
- RR 2.15.0 released · 2012-03-30
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- RR 2.14.0 released · 2011-10-31
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- RR 2.13.0 released · 2011-04-13
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- 1.02010-12-13
- RR 2.12.0 released · 2010-10-15
Package metadata
- First published
- 2010-12-13
- Total releases
- 19 / 16 yrs
- License
- GPL (>= 2) OSI
- Download size
- 24 KB
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