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

Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach

Current grammar-based NeuroEvolution approaches have several shortcomings. On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs composed of more than one hidden-layer. On the other, there is no way to evolve networks with more than one output neuron. To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers. In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations. By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron. Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons

Metaheuristics for black-box robust optimisation problems

Our interest is in the development of algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly (implementation uncertainty) the aim is to find a robust one. Here that is to find a point in the decision variable space such that the worst solution from within an uncertainty region around that point still performs well. This thesis comprises three research papers. One has been published, one accepted for publication, and one submitted for publication. We initially develop a single-solution based approach, largest empty hypersphere (LEH), which identifies poor performing points in the decision variable space and repeatedly moves to the centre of the region devoid of all such points. Building on this we develop population based approaches using a particle swarm optimisation (PSO) framework. This combines elements of the LEH approach, a local descent directions (d.d.) approach for robust problems, and a series of novel features. Finally we employ an automatic generation of algorithms technique, genetic programming (GP), to evolve a population of PSO based heuristics for robust problems. We generate algorithmic sub-components, the design rules by which they are combined to form complete heuristics, and an evolutionary GP framework. The best performing heuristics are identified. With the development of each heuristic we perform experimental testing against comparator approaches on a suite of robust test problems of dimension between 2D and 100D. Performance is shown to improve with each new heuristic. Furthermore the generation of large numbers of heuristics in the GP process enables an assessment of the best performing sub-components. This can be used to indicate the desirable features of an effective heuristic for tackling the problem under consideration. Good performance is observed for the following characteristics: inner maximisation by random sampling, a small number of inner points, particle level stopping conditions, a small swarm size, a Global topology, and particle movement using a baseline inertia formulation augmented by LEH and d.d. capabilities

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

Learning Behavior Trees with Genetic Programming in Unpredictable Environments

Modern industrial applications require robots to be able to operate in unpredictable environments, and programs to be created with a minimal effort, as there may be frequent changes to the task. In this paper, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. Moreover, we propose to use a simple simulator for the learning and demonstrate that the learned BTs can solve the same task in a realistic simulator, reaching convergence without the need for task specific heuristics. The learned solution is tolerant to faults, making our method appealing for real robotic applications