11,335 research outputs found
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
Optimizing genetic algorithm strategies for evolving networks
This paper explores the use of genetic algorithms for the design of networks,
where the demands on the network fluctuate in time. For varying network
constraints, we find the best network using the standard genetic algorithm
operators such as inversion, mutation and crossover. We also examine how the
choice of genetic algorithm operators affects the quality of the best network
found. Such networks typically contain redundancy in servers, where several
servers perform the same task and pleiotropy, where servers perform multiple
tasks. We explore this trade-off between pleiotropy versus redundancy on the
cost versus reliability as a measure of the quality of the network.Comment: 9 pages, 5 figure
Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems.
We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort
Postcopulatory sexual selection
The female reproductive tract is where competition between the sperm of different males takes place, aided and abetted by the female herself. Intense postcopulatory sexual selection fosters inter-sexual conflict and drives rapid evolutionary change to generate a startling diversity of morphological, behavioural and physiological adaptations. We identify three main issues that should be resolved to advance our understanding of postcopulatory sexual selection. We need to determine the genetic basis of different male fertility traits and female traits that mediate sperm selection; identify the genes or genomic regions that control these traits; and establish the coevolutionary trajectory of sexes
Multiobjective gas turbine engine controller design using genetic algorithms
This paper describes the use of multiobjective genetic algorithms (MOGAs) in the design of a multivariable control system for a gas turbine engine. The mechanisms employed to facilitate multiobjective search with the genetic algorithm are described with the aid of an example. It is shown that the MOGA confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This allows the engineer to examine the trade-offs between the different design objectives and configurations during the course of an optimization. In addition, the paper demonstrates how the genetic algorithm can be used to search in both controller structure and parameter space thereby offering a potentially more general approach to optimization in controller design than traditional numerical methods. While the example in the paper deals with control system design, the approach described can be expected to be applicable to more general problems in the fields of computer aided design (CAD) and computer aided engineering (CAE
Extended Inclusive Fitness Theory bridges Economics and Biology through a common understanding of Social Synergy
Inclusive Fitness Theory (IFT) was proposed half a century ago by W.D.
Hamilton to explain the emergence and maintenance of cooperation between
individuals that allows the existence of society. Contemporary evolutionary
ecology identified several factors that increase inclusive fitness, in addition
to kin-selection, such as assortation or homophily, and social synergies
triggered by cooperation. Here we propose an Extend Inclusive Fitness Theory
(EIFT) that includes in the fitness calculation all direct and indirect
benefits an agent obtains by its own actions, and through interactions with kin
and with genetically unrelated individuals. This formulation focuses on the
sustainable cost/benefit threshold ratio of cooperation and on the probability
of agents sharing mutually compatible memes or genes. This broader description
of the nature of social dynamics allows to compare the evolution of cooperation
among kin and non-kin, intra- and inter-specific cooperation, co-evolution, the
emergence of symbioses, of social synergies, and the emergence of division of
labor. EIFT promotes interdisciplinary cross fertilization of ideas by allowing
to describe the role for division of labor in the emergence of social
synergies, providing an integrated framework for the study of both, biological
evolution of social behavior and economic market dynamics.Comment: Bioeconomics, Synergy, Complexit
- …