Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.Comment: In GECCO 201

Interactive Evolution of Complex Behaviours Through Skill Encapsulation

Human-based computation (HBC) is an emerging research area in which humans and machines collaborate to solve tasks that neither one can solve in isolation. In evolutionary computation, HBC is often realized through interactive evolutionary computation (IEC), in which a user guides evolution by iteratively selecting the parents for the next generation. IEC has shown promise in a variety of different domains, but evolving more complex or hierarchically composed behaviours remains challenging with the traditional IEC approach. To overcome this challenge, this paper combines the recently introduced ESP (encapsulation, syllabus and pandemonium) algorithm with IEC to allow users to intuitively break complex challenges into smaller pieces and preserve, reuse and combine interactively evolved sub-skills. The combination of ESP principles with IEC provides a new way in which human insights can be leveraged in evolutionary computation and, as the results in this paper show, IEC-ESP is able to solve complex control problems that are challenging for a traditional fitness-based approach

Evolving Robot Controllers for Structured Environments Through Environment Decomposition

In this paper we aim to develop a controller that allows a robot to traverse an structured environment. The approach we use is to decompose the environment into simple sub-environments that we use as basis for evolving the controller. Specifically, we decompose a narrow corridor environment into four different sub-environments and evolve controllers that generalize to traverse two larger environments composed of the sub-environments. We also study two strategies for presenting the sub-environments to the evolutionary algorithm: all sub-environments at the same time and in sequence. Results show that by using a sequence the evolutionary algorithm can find a controller that performs well in all sub-environments more consistently than when presenting all sub-environments together. We conclude that environment decomposition is an useful approach for evolving controllers for structured environments and that the order in which the decomposed sub-environments are presented in sequence impacts the performance of the evolutionary algorithm

Search for Second-Generation Scalar Leptoquarks in ppˉ\bm{p \bar{p}} Collisions at s\sqrt{s}=1.96 TeV

Results on a search for pair production of second generation scalar leptoquark in $p \bar{p}$ collisions at $\sqrt{s}$=1.96 TeV are reported. The data analyzed were collected by the CDF detector during the 2002-2003 Tevatron Run II and correspond to an integrated luminosity of 198 pb$^{-1}$. Leptoquarks (LQ) are sought through their decay into (charged) leptons and quarks, with final state signatures represented by two muons and jets and one muon, large transverse missing energy and jets. We observe no evidence for $LQ$ production and derive 95% C.L. upper limits on the $LQ$ production cross sections as well as lower limits on their mass as a function of $\beta$, where $\beta$ is the branching fraction for $LQ \to \mu q$.Comment: 9 pages (3 author list) 5 figure