Using Prior Knowledge and Learning from Experience in Estimation of Distribution Algorithms

Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. One of the primary advantages of EDAs over many other stochastic optimization techniques is that after each run they leave behind a sequence of probabilistic models describing useful decompositions of the problem. This sequence of models can be seen as a roadmap of how the EDA solves the problem. While this roadmap holds a great deal of information about the problem, until recently this information has largely been ignored. My thesis is that it is possible to exploit this information to speed up problem solving in EDAs in a principled way. The main contribution of this dissertation will be to show that there are multiple ways to exploit this problem-specific knowledge. Most importantly, it can be done in a principled way such that these methods lead to substantial speedups without requiring parameter tuning or hand-inspection of models

GeNeSys - sistema de co-evolución genética y neuro-memética para la auto-organización senso-motriz y conductual en una sociedad de robots

Bio-inspired computing can be used to model natural and social systems, including societies with cultural development. Currently, two positions on cultural evolution stand out: with and without replicators. The existence of memes, as cultural replicators, is still hypothetical, and it seems better to look for them in the brain, because they can only be: neuro-memes. In literature there are only two models inspired by the neuro-memetics, and culture evolves side by side with genetics, so it’s necessary to model a gene-culture co-evolution, with neuro-memes. Such a model would be used to help validate the neuro-memetics, on the one hand, and on the other hand, it would help to understand and heal serious problems in human societies. Here, a genetic and neuro-memetic co-evolutionary system was achieved, and a robotic society used it for survive by developing behavioural patterns as a cultural tradition.La computación bio-inspirada puede ser empleada para modelar sistemas naturales y sociales, entre los cuales están las sociedades con desarrollo cultural. En la actualidad, sobresalen dos posturas sobre la evolución cultural: con y sin replicadores. La existencia de memes, como replicadores culturales, es aún hipotética, y parece mejor buscarlos en el cerebro, porque solo pueden ser: neuro-memes. En la literatura hay apenas dos modelos inspirados en la concepción neuro-memética, y como la evolución cultural va de la mano con la genética, se requiere entonces modelar una co-evolución gene-cultura, basada en neuro-memes. Un modelo así, se usaría para ayudar a validar la hipótesis neuro-memética, por un lado, y por el otro, ayudaría a comprender y atender serias problemáticas en las sociedades humanas. Con este proyecto se logró un sistema de co-evolución genética y neuro-memética, que fue usado por una sociedad de robots para sobrevivir, desarrollando un comportamiento cultural.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí

Effects of a Deterministic Hill climber on hBOA

Hybridization of global and local search algorithms is a well-established technique for enhancing the efficiency of search algorithms. Hybridizing estimation of distribution algorithms (EDAs) has been repeatedly shown to produce better performance than either the global or local search algorithm alone. The hierarchical Bayesian optimization algorithm (hBOA) is an advanced EDA which has previously been shown to benefit from hybridization with a local searcher. This paper examines the effects of combining hBOA with a deterministic hill climber (DHC). Experiments reveal that allowing DHC to find the local optima makes model building and decision making much easier for hBOA. This reduces the minimum population size required to find the global optimum, which substantially improves overall performance