Studying Organisms with Basic Cognitive Capacities in Artificial Worlds

In this paper we pose the problem of how to study basic cognitive processes in the frame of simulations of artificial worlds of the style of Artificial Life. The main difficulty of simulating biologically grounded cognitive processes lies in the search for forms of organisms suitable to establish functional relationships with their environments and coevolve with them. In order to attempt it, we study the properties of autonomous systems at different degrees of complexity and the origin of cognitive processes as a sophistication of primitive sensori-motor loops of living systems. The distinction between what we call ontogenetic adaptation to an environment and learning motivates a definition of two different degrees of complexity of that interaction. While the first one generates a variety of structures within individuals in an evolutionary scale, the second one produces a subsystem that is modulated during the life of each organism. We present some ideas to develop a model of an Artificial World where some of our theoretical claims can be studied, and suggest that an Artificial Life approach can arise an interesting discussion in Cognitive Science.Dans cet article on se pose la question suivante : comment étudier les processus cognitifs élémentaires dans le cadre des simulations des mondes artificiels. La difficulté majeure pour la simulation de processus cognitifs à fondement biologique demeure la recherche d'organismes susceptibles d'établir des rapports fonctionnels avec leur environnement et co-évoluer avec lui. Pour atteindre cet objectif, on étudie les propriétés des systèmes autonomes à différents degrés de complexité, et les origines des processus cognitifs en tant qu'une sophistication des boucles sensori-motrices primitives des systèmes vivants. La distinction entre ce qu'on appelle ici adaptation ontogénétique à son environnement et apprentissage nous conduit à définir deux différents degrés de complexité dans cette interaction. Tandis que le premier cas donne lieu à une variété de structures dans les individus à une échelle évolutive, le second produit un sous-système qui est modulé pendant la vie de chaque organisme. Finalement, on présente quelques idées pour permettre le développement d'un modèle d'un monde artificiel dans lequel certaines de nos hypothèses peuvent être étudiées et on suggère qu'une approche basée sur la méthodologie de la vie artificielle peut soulever une discussion intéressante dans le domaine des sciences cognitives.Etxeberria Arantza, Julian Merelo Juan, Moreno Alvaro. Studying Organisms with Basic Cognitive Capacities in Artificial Worlds. In: Intellectica. Revue de l'Association pour la Recherche Cognitive, n°18, 1994/1. Apprentissage et argumentation. pp. 45-69

Genealogical patterns in evolutionary algorithms

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Genealogical patterns in evolutionary algorithms

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Dynamic and Partially Connected Ring Topologies for Evolutionary Algorithms with Structured Populations

This paper investigates dynamic and partially connected ring topologies for cellular Evolutionary Algorithms (cEA). We hypothesize that these structures maintain population diversity at a higher level and reduce the risk of premature convergence to local optima on deceptive, multimodal and NP-hard fitness landscapes. A general framework for modelling partially connected topologies is proposed and three different schemes are tested. The results show that the structures improve the rate of convergence to global optima when compared to cEAs with standard topologies (ring, rectangular and square) on quasi-deceptive, deceptive and NP-hard problems. Optimal population size tests demonstrate that the proposed topologies require smaller populations when compared to traditional cEAs

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained

Revisiting Population Structure and Particle Swarm Performance

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Pool vs. island based evolutionary algorithms: an initial exploration

This paper explores the scalability and performance of pool and island based evolutionary algorithms, both of them using as a mean of interaction an object store; we call this family of algorithms SofEA. This object store allows the different clients to interact asynchronously; the point of the creation of this framework is to build a system for spontaneous and voluntary distributed evolutionary computation. The fact that each client is autonomous leads to a complex behavior that will be examined in the work, so that the design can be validated, rules of thumb can be extracted, and the limits of scalability can be found. In this paper we advance the design of an asynchronous, fault-tolerant and scalable distributed evolutionary algorithm based on the object store CouchDB. We test experimentally the different options and show the trade-offs that pool and island-based solutions offer

Improving the algorithmic efficiency and performance of channel-based evolutionary algorithms

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