Ensemble Learning for Free with Evolutionary Algorithms ?

Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles

Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called divergent discriminative feature accumulation (DDFA) that instead continually accumulates features that make novel discriminations among the training set. Thus DDFA features are inherently discriminative from the start even though they are trained without knowledge of the ultimate classification problem. Interestingly, DDFA also continues to add new features indefinitely (so it does not depend on a hidden layer size), is not based on minimizing error, and is inherently divergent instead of convergent, thereby providing a unique direction of research for unsupervised feature learning. In this paper the quality of its learned features is demonstrated on the MNIST dataset, where its performance confirms that indeed DDFA is a viable technique for learning useful features.Comment: Corrected citation formattin

Evolving a Behavioral Repertoire for a Walking Robot

Numerous algorithms have been proposed to allow legged robots to learn to walk. However, the vast majority of these algorithms is devised to learn to walk in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which com-bines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of con-trollers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution opens a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.Comment: 33 pages; Evolutionary Computation Journal 201

Expression-based evolution of faces

[Abstract] The combination of a classifier system with an evolutionary image generation engine is explored. The framework is instantiated using an off-the-shelf face detection system and a general purpose, expression-based, genetic programming engine. By default, the classifier returns a binary output, which is inadequate to guide evolution. By retrieving information provided by intermediate results of the classification task, it became possible to develop a suitable fitness function. The experimental results show the ability of the system to evolve images that are classified as faces. A subjective analysis also reveals the unexpected nature and artistic potential of the evolved images.Portugal. Fundação para a Ciência e a Tecnologia; PTDC/EIA–EIA/115667/2009Ministerio de Ciencia y Tecnología; TIN2008–06562/TINGalicia. Consellería de Innovación, Industria e Comercio; PGIDIT10TIC105008P

The diversity-accuracy duality in ensembles of classifiersd

Horizontal scaling of Machine Learning algorithms has the potential to tackle concerns over the scalability and sustainability of Deep Learning methods, viz. their consumption of energy and computational resources, as well their increasing inaccessibility to researchers. One way to enact horizontal scaling is by employing ensemble learning methods, since they enable distribution. There is a consensus on the point that diversity between individual learners leads to better performance, which is why we have focused on it as the criterion for distributing the base models of an ensemble. However, there is no standard agreement on how diversity should be defined and thus how to exploit it to construct a high-performing classifier. Therefore, we have proposed different definitions of diversity and innovative algorithms which promote it in a systematic way. We have first considered architectural diversity with an algorithm called WILDA: Wide Learning of Diverse Architectures. In a distributed fashion, this algorithm evolves a set of neural networks that are pretrained on the target task and diverse w.r.t. architectural feature descriptors. We have then generalised this notion by defining behavioural diversity on the basis of the divergence between the errors made by different models on a dataset. We have defined several diversity metrics and used them to guide a novelty search algorithm which builds an ensemble of behaviourally diverse classifiers. The algorithm promotes diversity in ensembles by explicitly searching for it, without selecting for accuracy. We have then extended this approach with a surrogate diversity model, which reduces the computational burden of this search by eliminating the need to train each network in the population with stochastic gradient descent at each step. These methods have enabled us to investigate the role that both architectural and behavioural diversity play in contributing to the performance of an ensemble. In order to study the relationship between diversity and accuracy in classifier ensembles, we have then proposed several methods that extend the novelty search with accuracy objectives. Surprisingly, we have observed that, with the highest-performing diversity metrics, there is an equivalence between searching for diversity objectives and searching for accuracy objectives. This contradicts widespread assumptions that a trade-off must be found by balancing diversity and accuracy objectives. We therefore posit the existence of a diversity-accuracy duality in ensembles of classifiers. An implication of this is the possibility of evolving diverse ensembles without detriment to their accuracy, since it is implicitly ensured.Open Acces

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

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