Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the$(1 + lambda)$evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Evolvability and redundancy in shared grammar evolution

Los Alamitos, C

Evolvability and organismal architecture:The blind watchmaker and the reminiscent architect

Organisms are constantly faced with the challenge of adapting to new circumstances. In this thesis, I argue that the ability to adapt to new circumstances, “evolvability”, is deeply ingrained in the genetic, developmental, morphological, and physiological architecture of organisms. Using a blend of conceptual research, theoretical modelling, and multidisciplinary studies, I demonstrate how organismal architecture can evolve so that organisms can cope better and better with future environmental challenges. As a first step, I systematically classify the many factors contributing to evolvability. Then I use a simulation approach to show how evolvability-enhancing structures can readily evolve in gene-regulatory networks. This happens via the evolution of "mutational transformers" - structural elements that convert random mutations at the genetic level into adaptation-enhancing mutations at the phenotypic level. In another thesis chapter, I demonstrate that even if selection acts only sporadically, complex adaptations can evolve and persist over long time periods. In other words, complex adaptations do not require constant selection pressure. In an interdisciplinary contribution, I apply biological insights regarding the properties of an evolvability-enhancing mutation structure to the design of algorithms used in Artificial Intelligence. The result is the “Facilitated Mutation” method which enhances the performance of the algorithms in various respects, highlighting the potential for leveraging biological principles in computational sciences. Finally, I embed my research findings in a philosophical context. I emphasise the importance of organismal architecture in retaining evolutionary memories and suggest future research directions to further enhance our understanding of evolvability

Gestures and Adaptive Niches: an Evolutionary Perspective on Co-speech Gestures

This proposal presents an evolutionary analysis of three types of co-speech gestures: symbolic emblems, indexical pointing gestures and iconic representational gesticulations. Synthesizing insights from a range of published sources in gestural studies, general linguistics and sign language linguistics, primate studies and analyses of biological evolution, these gestures are analyzed as evolved traits adapted to particular niches or roles within broader systems. Niche boundaries are comprised of an element’s distinct properties and functions, routes of learning and transmission and degrees of innateness and evolvability within populations. Rather than elements distributed along a flat productive-analytical continuum or as stages along diachronic pathways, these gestural traits are analyzed in terms of adaptive peaks and valleys with a landscape representing the broader system comprising human gesture and language. The same evolutionary processes are used to analyze gestures in speaking populations and the linguistic traits derived from gestures in signing populations. This approach offers new ways of approaching proposed linguistic universals and long-standing issues such as listability in sign languages, while offering a formal approach to gestures

A Naturalist Reconstruction of Minimalist and Evolutionary Biolinguistics

Kinsella & Marcus (2009; K&M) argue that considerations of biological evolution invalidate the picture of optimal language design put forward under the rubric of the minimalist program (Chomsky 1993 et seq.), but in this article it will be pointed out that K&M’s objection is undermined by (i) their misunderstanding of minimalism as imposing an aprioristic presumption of optimality and (ii) their failure to discuss the third factor of language design. It is proposed that the essence of K&M’s suggestion be reconstructed as the sound warning that one should refrain from any preconceptions about the object of inquiry, to which K&M commit themselves based on their biased view of evolution. A different reflection will be cast on the current minimalist literature, arguably along the lines K&M envisaged but never completed, converging on a recommendation of methodological (and, in a somewhat unconventional sense, metaphysical) naturalism