Ivan Tanev

Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.

Hybrid evolutionary algorithm-based real-world flexible job shop scheduling problem: application service provider approach

Abstract This paper presents an approach for scheduling of customers’ orders in factories of plastic injection machines (FPIM) as a case of real-world flexible job shop scheduling problem. The objective of discussed work is to provide FPIM with high business speed which implies (a) providing a customers with convenient way for remote online access to the factory’s database and (b) developing an efficient scheduling routine for planning the assignment of the submitted customers’ orders to FPIM machines. Remote online access to FPIM database, approached via delivering the software as a Web-service in accordance with the application service provider (ASP) paradigm is proposed. As an approach addressing the issue of efficient scheduling routine a hybrid evolutionary algorithm (HEA) combining priority-dispatching rules (PDRs) with GA is developed. An implementation of HEA as a database stored procedure is discussed. Performance evaluation results are presented. The results obtained for evolving a schedule of 400 customers’ orders on experimental model of FPIM indicate that the business delays in order of half-an-hour can be achieved.

DOM/XML-based portable genetic representation of the morphology, behavior and communication abilities of evolvable agents

This article presents the results of our work on the role of genetic representation in facilitating the quick design of efficiently running offline learning via genetic programming (GP). An approach using the widely adopted document object model/extensible mark-up language (DOM/XML) standard for the representation of genetic programs, and off-the-shelf DOM-parsers with built-in application programming interface (API) for manipulating them is proposed. This approach means a significant reduction in time in the usually slow software engineering of GP, and offers a generic way to facilitate the reduction of computational effort by limiting the search space of genetic programming by handling only semantically correct genetic programs. The concept is accomplished through strongly typed genetic programming (STGP), in which the use of W3C-recommended standard XML schema is proposed as a generic way to represent and impose the grammar rules in STGP. The ideas laid in the foundation of the proposed approach are verified by the implementation of GP in the evolving social behavior of agents in predator–prey pursuit problems.

Evolution of the driving styles of anticipatory agent remotely operating a scaled model of racing car

We present an approach for automated evolutionary design of driving agent, able to remotely operate a scale model of racing car running in a fastest possible way. The agents actions are conveyed to the car via standard radio control transmitter. The agent perceives the environment from a live video feedback of an overhead camera. In order to cope with the inherent video feed latency, which renders even the straightforward tasks of following simple routes unsolvable, we implement an anticipatory modeling - the agent considers its current actions based on anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. The driving style (i.e. the driving line combined with the speed at which the car travels along this line) is first evolved offline on a software simulator of the car and then adapted online to the real world. Experimental results demonstrate that on long runs the agent-operated car is only marginally slower than a human-operated one, while the consistence of lap times posted by the evolved driving style of the agent is better than that of a human. This work can be viewed as a step towards the development of a framework for automated design of the controllers of remotely operated vehicles capable to find an optimal solution to various tasks in different traffic situations and road conditions.

XML-based genetic programming framework: design philosophy, implementation, and applications

We present the design philosophy, implementation, and various applications of an XML-based genetic programming (GP) framework (XGP). The key feature of XGP is the distinct representation of genetic programs as DOM parsing trees featuring corresponding flat XML text. XGP contributes to the achievements of: (i) fast prototyping of GP by using the standard built-in API of DOM parsers for manipulating the genetic programs, (ii) human readability and modifiability of the genetic representations, (iii) generic support for the representation of the grammar of a strongly typed GP using W3C-standardized XML schema; (iv) inherent inter-machine migratability of the text-based genetic representation (i.e., the XML text) in the distributed implementations of GP.

Scalable architecture for parallel distributed implementation of genetic programming on network of workstations

Abstract We present an approach for developing a scalable architecture for parallel distributed implementation of genetic programming (PDIGP). The approach is based on exploitation of the inherent parallelism among semi-isolated subpopulations in genetic programming (GP). Proposed implementation runs on cost-efficient configurations of networks on workstations in LAN and Internet environment. Developed architecture features single global migration broker and centralized manager of the semi-isolated subpopulations, which contribute to achieving quick propagation of the globally fittest individuals among the subpopulations, reducing the performance demands to the communication network, and achieving flexibility in system configurations by introducing dynamically scaling up opportunities. PDIGP exploits distributed component object model (DCOM) as a communication paradigm, which as a true system model offers generic support for the issues of naming, locating and protecting the distributed entities in proposed architecture of PDIGP. Experimentally obtained results of computational effort of proposed PDIGP are discussed. The results show that computational effort of PDIGP marginally differs from the computational effort in canonical panmictic GP evolving single large population. For PDIGP running on systems configurations with 16 workstations the computational effort is less than panmictic GP, while for smaller configurations it is insignificantly more. Analytically obtained and empirically proved results of the speedup of computational performance indicate that PDIGP features linear, close to ideal characteristics. Experimentally obtained results of PDIGP running on configurations with eight workstations show close to 8-fold overall speedup. These results are consistent with the anticipated cumulative effect of the insignificant increase of computational effort for the considered configuration and the close to linear speedup of computational performance.

Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones

We present the results of our work in simulating the recently discovered findings in molecular biology regarding the significant role which histones play in regulating the gene expression in eukaryotes. Extending the notion of inheritable genotype in evolutionary computation from the commonly considered model of DNA to chromatin (DNA and histones), we present epigenetic programming as an approach, incorporating an explicitly controlled gene expression through modification of histones in strongly-typed genetic programming (STGP). We propose a double cell representation of the simulated individuals, comprising somatic cell and germ cell, both represented by their respective chromatin structures. Following biologically plausible concepts, we regard the plastic phenotype of the somatic cell, achieved via controlled gene expression owing to modifications to histones (epigenetic learning, EL) as relevant for fitness evaluation, while the genotype of the germ cell corresponds to the phylogenesis of the individuals. The beneficial effect of EL on the performance characteristics of STGP is verified on evolution of social behavior of a team of predator agents in the predator-prey pursuit problem. Empirically obtained performance evaluation results indicate that EL contributes to about 2-fold improvement of computational effort of STGP. We trace the cause for that to the cumulative effect of polyphenism and epigenetic stability, both contributed by EL. The former allows for phenotypic diversity of genotypically similar individuals, while the latter robustly preserves the individuals from the destructive effects of crossover by silencing certain genotypic combinations and explicitly activating them only when they are most likely to be expressed in corresponding beneficial phenotypic traits.

Co-evolution of active sensing and locomotion gaits of simulated snake-like robot

We propose an approach of automated co-evolution of the optimal values of attributes of active sensing (orientation, range and timing of activation of sensors) and the control of locomotion gaits of simulated snake-like robot (Snakebot) that result in a fast speed of locomotion in a confined environment. The experimental results illustrate the emergence of a contactless wall-following navigation of fast sidewinding Snakebots. The wall-following is accomplished by means of differential steering, facilitated by the evolutionary defined control sequences incorporating the readings of evolutionary optimized sensors.

Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot

In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained results verify that employing LPCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of the Snakebot.

Incorporating learning probabilistic context-sensitive grammar in genetic programming for efficient evolution and adaptation of snakebot

In this work we propose an approach of incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP) employed for evolution and adaptation of locomotion gaits of simulated snake-like robot (Snakebot). In our approach LPCSG is derived from the originally defined context-free grammar, which usually expresses the syntax of genetic programs in canonical GP. During the especially introduced steered mutation the probabilities of applying each of particular production rules with multiple right-hand side alternatives in LPCSG depend on the context, and these probabilities are learned from the aggregated reward values obtained from the evolved best-of-generation Snakebots. Empirically obtained results verify that employing LPCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of Snakebot. In all of the cases considered in this study, the locomotion gaits, evolved and adapted employing GP with LPCSG feature higher velocity and are obtained faster than with canonical GP.