Abstract. Bedau and Packard’s evolutionary activity statistics [1, 2] are used to classify the evolutionary dynamics in Geb [3, 4], a system designed to verify and extend theories behind the generation of evolutionary emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary activity, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. Two weaknesses are identified and approaches for overcoming them are proposed. 1

Genetic algorithms (GAs) are global, parallel, stochastic search methods, founded on Darwinian evolutionary principles. Many variations exist, including genetic programming and multiobj ective algorithms. During the last decade GAs have been applied in a variety of areas, with varying degrees of success within each. A significant contribution has been made within control systems engineering. GAs exhibit considerable robustness in problem domains that are not conducive to formal, rigorous, classical analysis. They are not limited by typical control problem attributes such as ill-behaved objective functions, the existence of constraints, and variations in the nature of control variables. GA software tools are available, but there is no 'industry standard'. The computational complexity of the GA has proved to be the chief impediment to real-time application of the technique. Hence, the majority of applications that use GAs are, by nature, off-line. GAs have been used to optimise both structure and parameter values for both controllers and plant models. They have also been applied to fault diagnosis, stability analysis, robot path-planning, and combinatorial problems (such as scheduling and bin-packing). Hybrid approaches have proved popular, with GAs being integrated in fuzzy logic and neural computing schemes. The GA has been used as the population-based engine for multiobjective optimisers. Multiple, Pareto-optimal, solutions can be represented simultaneously. In such schemes, a decision-maker can lead the direction of future search. Interesting future developments are anticipated in on-line applications and multiobjective search and decision-making.

Bedau et al.’s statistical classification system for long-term evolutionary dynamics provides a test for open-ended evolution. Making this test more rigorous, and passing it, are two of the most important open problems for research into systems of agents that actively process and transform their environment. This paper presents a detailed description of the application of this test to ‘Geb’, a system designed to verify and extend theories behind the generation of evolutionarily emergent systems. The result is that, according to these statistics, Geb exhibits unbounded evolutionary dynamics, making it the first autonomous artificial system to pass this test. However, having passed it, the most prudent course of action is to look for weaknesses in the test. The test is criticized, most significantly with regard to its normalization method for artificial systems. Furthermore, this paper presents a modified normalization method, based on component activity normalization, that overcomes these criticisms. The results of the revised test, when applied to Geb, indicate that this system does indeed exhibit open-ended evolution.

Abstract not found

The design of electromagnetic systems using methods of optimization have been carried out with deterministic methods. However, these methods are not efficient, because the object functions obtained from electromagnetic optimization problems are often highly non-linear, stiff, multiextreme and non-differential. The lack of a single method available to deal with multidimensional problems, including those with several goals to optimize, has generated the need to use numerical processes for optimization. This paper presents a method of global optimization based on genetic algorithms. The Genetic Algorithms are a versatile tool, which can be applied as a global optimization method to problems of electromagnetic engineering, because they are easy to implement to non-differentiable functions and discrete search spaces. It is also shown how, in some cases, genetic algorithms have been applied with success in electromagnetic problems, such as antenna design, far-field prediction, absorber coatings design, etc.

Abstract—Biological evolution has generated a rich variety of successful solutions; from nature, optimized strategies can be inspired. One interesting example is the ant colonies, which are able to exhibit a collective intelligence, still that their dynamic is simple. The emergence of different patterns depends on the pheromone trail, leaved by the foragers. It serves as positive feedback mechanism for sharing information. In this paper, we use the dynamic of TASEP as a model of interaction at a low level of the collective environment in the ant’s traffic flow. This work consists of modifying the movement rules of particles “ants ” belonging to the TASEP model, so that it adopts with the natural movement of ants. Therefore, as to respect the constraints of having no more than one particle per a given site, and in order to avoid collision within a bidirectional circulation, we suggested two strategies: decease strategy and waiting strategy. As a third work stage, this is devoted to the study of these two proposed strategies’ stability.

The growing interest in the biological roots of cognition leads to the crossfertilization between the fields of autonomous robotics and artificial life. This requires new tools that facilitate research on the interface between embodied cognitive science and theoretical biology. In this thesis, a model is presented that enables the simulation of evolving ecosystems of situated agents. This model enables studies to the interplay between situated interaction, selforganised collective behaviour and evolution by natural selection. The use of complex computer simulations as scientific tools requires a theoretical embedding. This is established by analysing and interpreting the results of ecological simulations (of bitrophic and tritrophic food chains) in term of analytical models of population dynamics. These ordinary differential equation (ODE) models allow us to understand and control the population dynamics that emerge from simulations. Moreover, the evolutionary dynamics observed in eco-evolutionary simulations can be interpreted as changes in the ODE model, which enables us to understand evolvability of certain traits in