The Microbial Genetic Algorithm

  title={The Microbial Genetic Algorithm},
  author={I. Harvey},
  • I. Harvey
  • Published in ECAL 2009
  • Biology, Computer Science
We analyse how the conventional Genetic Algorithm can be stripped down and reduced to its basics. We present a minimal, modified version that can be interpreted in terms of horizontal gene transfer, as in bacterial conjugation. Whilst its functionality is effectively similar to the conventional version, it is much easier to program, and recommended for both teaching purposes and practical applications. Despite the simplicity of the core code, it effects Selection, (variable rates of… Expand
Investigation into mutation operators for Microbial Genetic Algorithm
  • Samreen Umer
  • Computer Science
  • 2015 7th International Joint Conference on Computational Intelligence (IJCCI)
  • 2015
This paper discussed and analyzed variants of this less exploited algorithm on known benchmark testing functions to suggest a suitable choice of mutation operator and proposed a simple adaptive scheme to adjust the impact of mutation according to the diversity in population in a cost effective way. Expand
The Bacterial Evolutionary Algorithm (BEA) is a relatively new type of evolutionary algorithm and shows the typical phenomena of stochastic optimization methods. Two of these phenomena: prematureExpand
Critical mutation rate in a population with horizontal gene transfer
A genetic algorithm designed to model the process of bacterial evolution in a fitness landscape in which individuals with greater mutational robustness can outcompete those with higher fitness when a critical mutation rate (CMR) is exceeded is presented. Expand
Comparative Analysis of Parallel Gene Transfer Operators in the Bacterial Evolutionary Algorithm
The Bacterial Evolutionary Algorithm (BEA) is an evolutionary method, originally meant to optimize the parameters of fuzzy systems. The authors have already proposed three modified versions of theExpand
Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem
It is concluded that artificial transposons, analogous to real transposon, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts. Expand
Binomics - Where Metagenomics meets the Binary World
It is proposed that Binomics, defined as computational algorithms inspired by Metagenomic studies, forms a potentially fruitful field of study waiting to be investigated. Expand
Unconstrain the Population: The Benefits of Horizontal Gene Transfer in Genetic Algorithms
The Unconstrained Genetic Algorithm (UGA) is presented which is a novel GA that takes its inspiration from bacterial evolution and shows how it outperforms a standard GA on a benchmark task with a large number of sub-optimal solutions. Expand
Evolving graphs with horizontal gene transfer
Experimental results from 14 symbolic regression benchmark problems show that the introduction of the µ × λ EA and HGT events improve the performance of EGGP. Expand
An Artificial Life Simulation Library Based on Genetic Algorithm, 3-Character Genetic Code and Biological Hierarchy
The design of a Python library for artificial life simulation, Digital Organism Simulation Environment (DOSE), based on GA and biological hierarchy starting from genetic sequence to population is described. Expand
How Fast Can We Evolve Something?
A rough and ready heuristic for estimating how many generations one should reasonably expect to wait before reaching an acceptable solution to a Genetic Algorithm problem is presented. Expand


The Microbial Genetic Algorithm the Microbial Genetic Algorithm
The genetic algorithm is simpliied to a minimal form which retains selection, recombination and mutation. This introduces the idea of bacterial recombination, or infection, as a substitute forExpand
Artificial Evolution: A Continuing SAGA
The basic principles of SAGA (Species Adaptation GAs) will be outlined, and the concept of Neutral Networks, pathways of level fitness through a fitness landscape will be introduced. Expand
Trivial Geography in Genetic Programming
Geographical distribution is widely held to be a major determinant of evolutionary dynamics. Correspondingly, genetic programming theorists and practitioners have long developed, used, and studiedExpand
Genetic Programming Theory and Practice III
This book discusses the development of Genetic Programming in Industrial Analog CAD, the challenges in Open-Ended Problem Solving with Genetic Programming, and the importance of local search in Genetic Programming. Expand
Embodied Evolution: Distributing an evolutionary algorithm in a population of robots
Embodied Evolution is introduced as a new methodology for evolutionary robotics that uses a population of physical robots that autonomously reproduce with one another while situated in their task environment and designs a fully decentralized, asynchronous evolutionary algorithm. Expand
Artificial Life, a Continuing SAGA
  • Gomi, T. (ed.) ER-EvoRob 2001. LNCS, vol. 2217, pp. 94–109. Springer, Heidelberg
  • 2001
Evolutionary Robotics
The Microbial Genetic Algorithm
  • Unpublished report
  • 1996