Onboard Evolution of Understandable Swarm Behaviors

Designing the individual robot rules that give rise to desired emergent swarm behaviors is difficult. The common method of running evolutionary algorithms off‐line to automatically discover controllers in simulation suffers from two disadvantages: the generation of controllers is not situated in the swarm and so cannot be performed in the wild, and the evolved controllers are often opaque and hard to understand. A swarm of robots with considerable on‐board processing power is used to move the evolutionary process into the swarm, providing a potential route to continuously generating swarm behaviors adapted to the environments and tasks at hand. By making the evolved controllers human‐understandable using behavior trees, the controllers can be queried, explained, and even improved by a human user. A swarm system capable of evolving and executing fit controllers entirely onboard physical robots in less than 15 min is demonstrated. One of the evolved controllers is then analyzed to explain its functionality. With the insights gained, a significant performance improvement in the evolved controller is engineered

Studying Parallel Evolutionary Algorithms: The cellular Programming Case

Parallel evolutionary algorithms, studied to some extent over the past few years, have proven empirically worthwhile—though there seems to be lacking a better understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, presenting a number of statistical measures, both at the genotypic and phenotypic levels. We demonstrate the application and utility of these measures on a specific example, that of the cellular programming evolutionary algorithm, when used to evolve solutions to a hard problem in the cellular-automata domain, known as synchronization

A service oriented architecture for decision making in engineering design

Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design

A new initialization procedure for the distributed estimation of distribution algorithms

Estimation of distribution algorithms (EDAs) are one of the most promising paradigms in today’s evolutionary computation. In this field, there has been an incipient activity in the so-called parallel estimation of distribution algorithms (pEDAs). One of these approaches is the distributed estimation of distribution algorithms (dEDAs). This paper introduces a new initialization mechanism for each of the populations of the islands based on the Voronoi cells. To analyze the results, a series of different experiments using the benchmark suite for the special session on Real-parameter Optimization of the IEEE CEC 2005 conference has been carried out. The results obtained suggest that the Voronoi initialization method considerably improves the performance obtained from a traditional uniform initialization

Human Action Recognition Based on Temporal Pyramid of Key Poses Using RGB-D Sensors

Human action recognition is a hot research topic in computer vision, mainly due to the high number of related applications, such as surveillance, human computer interaction, or assisted living. Low cost RGB-D sensors have been extensively used in this field. They can provide skeleton joints, which represent a compact and effective representation of the human posture. This work proposes an algorithm for human action recognition where the features are computed from skeleton joints. A sequence of skeleton features is represented as a set of key poses, from which histograms are extracted. The temporal structure of the sequence is kept using a temporal pyramid of key poses. Finally, a multi-class SVM performs the classification task. The algorithm optimization through evolutionary computation allows to reach results comparable to the state-of-the-art on the MSR Action3D dataset.This work was supported by a STSM Grant from COST Action IC1303 AAPELE - Architectures, Algorithms and Platforms for Enhanced Living Environments

A Dynamic Island-Based Genetic Algorithms Framework

This work presents a dynamic island model framework for helping the resolution of combinatorial optimization problems with evolutionary algorithms. In this framework, the possible migrations among islands are represented by a complete graph. The migrations probabilities associated to each edge are dynamically updated with respect to the last migrations impact. This new framework is tested on the well-known 0/1 Knapsack problem and MAX-SAT problem. Good results are obtained and several properties of this framework are studied

Structure Discovery in Mixed Order Hyper Networks

Background  Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human readability that networks with hidden units lack. They can be used for regression, classification or as content addressable memories and have been shown to be useful as fitness function models in constraint satisfaction tasks. They are fast to train and, when their structure is fixed, do not suffer from local minima in the cost function during training. However, their main drawback is that the correct structure (which neurons to connect with weights) must be discovered from data and an exhaustive search is not possible for networks of over around 30 inputs.  Results  This paper presents an algorithm designed to discover a set of weights that satisfy the joint constraints of low training error and a parsimonious model. The combined structure discovery and weight learning process was found to be faster, more accurate and have less variance than training an MLP.  Conclusions  There are a number of advantages to using higher order weights rather than hidden units in a neural network but discovering the correct structure for those weights can be challenging. With the method proposed in this paper, the use of high order networks becomes tractable