Tournament versus Fitness Uniform Selection

In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In this paper we compare the relative performance of FUSS with the well known tournament selection scheme on a range of problems.Comment: 10 pages, 8 figure

Deception in Optimal Control

In this paper, we consider an adversarial scenario where one agent seeks to achieve an objective and its adversary seeks to learn the agent's intentions and prevent the agent from achieving its objective. The agent has an incentive to try to deceive the adversary about its intentions, while at the same time working to achieve its objective. The primary contribution of this paper is to introduce a mathematically rigorous framework for the notion of deception within the context of optimal control. The central notion introduced in the paper is that of a belief-induced reward: a reward dependent not only on the agent's state and action, but also adversary's beliefs. Design of an optimal deceptive strategy then becomes a question of optimal control design on the product of the agent's state space and the adversary's belief space. The proposed framework allows for deception to be defined in an arbitrary control system endowed with a reward function, as well as with additional specifications limiting the agent's control policy. In addition to defining deception, we discuss design of optimally deceptive strategies under uncertainties in agent's knowledge about the adversary's learning process. In the latter part of the paper, we focus on a setting where the agent's behavior is governed by a Markov decision process, and show that the design of optimally deceptive strategies under lack of knowledge about the adversary naturally reduces to previously discussed problems in control design on partially observable or uncertain Markov decision processes. Finally, we present two examples of deceptive strategies: a "cops and robbers" scenario and an example where an agent may use camouflage while moving. We show that optimally deceptive strategies in such examples follow the intuitive idea of how to deceive an adversary in the above settings

Novelty grammar swarms

Tese de mestrado, Engenharia Informática (Sistemas de Informação), Universidade de Lisboa, Faculdade de Ciências, 2015Particle Swarm Optimization (PSO) é um dos métodos de optimização populacionais mais conhecido. Normalmente é aplicado na otimização funções de fitness, que indicam o quão perto o algoritmo está de atingir o objectivo da pesquisa, fazendo com que esta se foque em áreas de fitness mais elevado. Em problemas com muitos ótimos locais, regularmente a pesquisa fica presa em locais com fitness elevado mas que não são o verdadeiro objetivo. Com vista a solucionar este problema em certos domínios, nesta tese é introduzido o Novelty-driven Particle Swarm Optimization (NdPSO). Este algoritmo é inspirado na pesquisa pela novidade (novelty search), um método relativamente recente que guia a pesquisa de forma a encontrar instâncias significativamente diferentes das anteriores. Desta forma, o NdPSO ignora por completo o objetivo perseguindo apenas a novidade, isto torna-o menos susceptivel a ser enganado em problemas com muitos optimos locais. Uma vez que o novelty search mostrou potencial a resolver tarefas no âmbito da programação genética, em particular na evolução gramatical, neste projeto o NdPSO é usado como uma extensão do método de Grammatical Swarm que é uma combinação do PSO com a programação genética. A implementação do NdPSO é testada em três domínios diferentes, representativos daqueles para o qual este algoritmo poderá ser mais vantajoso que os algoritmos guiados pelo objectivo. Isto é, domínios enganadores nos quais seja relativamente intuitivo descrever um comportamento. Em cada um dos domínios testados, o NdPSO supera o aloritmo standard do PSO, uma das suas variantes mais conhecidas (Barebones PSO) e a pesquisa aleatória, mostrando ser uma ferramenta promissora para resolver problemas enganadores. Uma vez que esta é a primeira aplicação da pesquisa por novidade fora do paradigma evolucionário, neste projecto é também efectuado um estudo comparativo do novo algoritmo com a forma mais comum de usar a pesquisa pela novidade (na forma de algoritmo evolucionário).Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize fitness functions that specify the goal of reaching a desired objective or behavior. As a result, search focuses on higher-fitness areas. In problems with many local optima, search often becomes stuck, and thus can fail to find the intended objective. To remedy this problem in certain kinds of domains, this thesis introduces Novelty-driven Particle Swarm Optimization (NdPSO). Taking motivation from the novelty search algorithm in evolutionary computation, in this method search is driven only towards finding instances significantly different from those found before. In this way, NdPSO completely ignores the objective in its pursuit of novelty, making it less susceptible to deception and local optima. Because novelty search has previously shown potential for solving tasks in Genetic Programming, particularly, in Grammatical Evolution, this paper implements NdPSO as an extension of the Grammatical Swarm method which in effect is a combination of PSO and Genetic Programming.The resulting NdPSO implementation was tested in three different domains representative of those in which it might provide advantage over objective-driven PSO, in particular, those which are deceptive and in which a meaningful high-level description of novel behavior is easy to derive. In each of the tested domains NdPSO outperforms both objective-based PSO and random-search, demonstrating its promise as a tool for solving deceptive problems. Since this is the first application of the search for novelty outside the evolutionary paradigm an empirical comparative study of the new algorithm to a standard novelty search Evolutionary Algorithm is performed

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty