Self modifying Cartesian Genetic Programming: Parity

Self Modifying Cartesian Genetic Programming (SMCGP) aims to be a general purpose form of developmental genetic programming. The evolved programs are iterated thus al-lowing an infinite sequence of phenotypes (programs) to be obtained from a single evolved genotype. In previous work this approach has already shown that it is possible to obtain mathematically provable general solutions to certain prob-lems. We extend this class in this paper by showing how SMCGP can be used to find algorithms that converge to mathematical constants (pi and e). Mathematical proofs are given that show that some evolved formulae converge to pi and e in the limit as the number of iterations increase

Emergence in genetic programming:let's exploit it!

Banzhaf explores the concept of emergence and how and where it happens in genetic programming [1]. Here we consider the question: what shall we do with it? We argue that given our ultimate goal to produce genetic programming systems that solve new and difficult problems, we should take advantage of emergence to get closer to this goal

Gardening Cyber-Physical Systems

cote interne IRCAM: Stepney12aNational audienceToday’s artefacts, from small devices to buildings and cities, are, or are becoming, cyber-physical socio-technical systems, with tightly interwoven material and computational parts. Currently, we have to la- boriously build such systems, component by component, and the results are often difficult to maintain, adapt, and reconfigure. Even “soft”ware is brittle and non-trivial to adapt and change. If we look to nature, how- ever, large complex organisms grow, adapt to their environment, and repair themselves when damaged. In this position paper, we present Gro-CyPhy, an unconventional computational framework for growing cyber-physical systems from com- putational seeds, and gardening the growing systems, in order to adapt them to specific needs. The Gro-CyPhy architecture comprises: a Seed Factory, a process for designing specific computational seeds to meet cyber-physical system requirements; a Growth Engine, providing the computational processes that grow seeds in simulation; and a Computational Garden, where mul- tiple seeds can be planted and grown in concert, and where a high-level gardener can shape them into complex cyber-physical systems. We outline how the Gro-CyPhy architecture might be applied to a significant exemplar application: a (simulated) skyscraper, comprising several mutually interdependent physical and virtual subsystems, such as the shell of exterior and interior walls, electrical power and data net- works, plumbing and rain-water harvesting, heating and air-conditioning systems, and building management control systems

Equidistant Reorder operator for Cartesian Genetic Programming

The Reorder operator, an extension to Cartesian Genetic Programming (CGP), eliminates limitations of the classic CGP algorithm by shuffling the genome. One of those limitations is the positional bias, a phenomenon in which mostly genes at the start of the genome contribute to an output, while genes at the end rarely do. This can lead to worse fitness or more training iterations needed to find a solution. To combat this problem, the existing Reorder operator shuffles the genome without changing its phenotypical encoding. However, we argue that Reorder may not fully eliminate the positional bias but only weaken its effects. By introducing a novel operator we name Equidistant-Reorder, we try to fully avoid the positional bias. Instead of shuffling the genome, active nodes are reordered equidistantly in the genome. Via this operator, we can show empirically on four Boolean benchmarks that the number of iterations needed until a solution is found decreases; and fewer nodes are needed to e fficiently find a solution, which potentially saves CPU time with each iteration. At last, we visually analyse the distribution of active nodes in the genomes. A potential decrease of the negative effects of the positional bias can be derived with our extension

Self-Modifying Programs in Cartesian Genetic Programming

Kartézské genetické programování se během posledních let ukázalo jako velmi perspektivní oblast evolučních výpočtů. Má však jistá omezení, která znemožňují řešit pomocí něj rozsáhlejší nebo obecné problémy. Tato omezení lze eliminovat pomocí novějšího přístupu umožňujícího sebemodifikaci programů v kartézském genetickém programování. Cílem této práce je zhodnotit dosavadní vývoj a aktuální situaci v této oblasti a navrhnout vlastní řešení různých problémů, při jejichž řešení klasické kartézské genetické programování selhává. Jedním z těchto problémů, kterými se práce zabývá, je generování členů Taylorova rozvoje pro různé funkce. Vzhledem k tomu, že se jedná o problém vyžadující zobecnění, je cílem dokázat, že sebemodifikující varianta kartézského genetického programování je v tomto ohledu lepší než klasická. Dalším řešeným problémem bude využití sebemodifikujících programů v kartézském genetickém programování k návrhu řadicích sítí pro libovolný počet vstupů. Také v tomto případě je záměrem dokázat, že sebemodifikace přináší do kartézského genetického programování nové aspekty nutné k vývoji libovolně rozsáhlých řešení.During the last years cartesian genetic programming proved to be a very perspective area of the evolutionary computing. However it has its limitations, which make its use in area of large and generic problems impossible. These limitations can be eliminated using the recent method allowing self-modification of programs in cartesian genetic programming. The purpose of this thesis is to review the development in this area done so far. Next objective is to design own solutions for solving various problems that are hardly solvable using the ordinary cartesian genetic programming. One of the problems to be considered is generating the terms of various Taylor series. Due to the fact that the solution to this problem requires generalisation, the goal is to prove that the self-modifying cartesian genetic programming scores better than classic one for this problem. Another discussed problem is using the self-modifying genetic programming for developing arbitrarily large sorting networks. In this case, the objective is to prove that self-modification brings new features to the cartesian genetic programming allowing the development of arbitrarily sized designs.