19,907 research outputs found
Intelligent Genetic Algorithms in Evolutionary Computation Part 1. Biological Foundation
In this paper, we review a large amount of historical biological literature [Darwin, 1862, 1871; Fisher, 1930 and others] and recent developments in biological [ Anderson, 1994] and biocomputational literature [Miller & Todd, 1992, 1994], try to integrate the dynamics of interplay between natural selection and sexual selection through mate choice in biology with evolutionary computation as a process of search, diversification and optimization and originate a new class of evolutionary algorithm which we term Intelligent Genetic Algorithms. These intelligent genetic algorithms demonstrate their effectiveness and efficiency in generating evolutionary innovations, maintaining genetic diversity, promoting mate choice and sexual recombination in species and guiding the movement of a population from local optima to global optima in parallel. Furthermore, we attempt to provide some common biological origins for these new Intelligent Genetic Algorithms
Regulatory motif discovery using a population clustering evolutionary algorithm
This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Self-adaptation of Genetic Operators Through Genetic Programming Techniques
Here we propose an evolutionary algorithm that self modifies its operators at
the same time that candidate solutions are evolved. This tackles convergence
and lack of diversity issues, leading to better solutions. Operators are
represented as trees and are evolved using genetic programming (GP) techniques.
The proposed approach is tested with real benchmark functions and an analysis
of operator evolution is provided.Comment: Presented in GECCO 201
- …