Multitask Evolution with Cartesian Genetic Programming

We introduce a genetic programming method for solving multiple Boolean circuit synthesis tasks simultaneously. This allows us to solve a set of elementary logic functions twice as easily as with a direct, single-task approach.Comment: 2 page

Differentiable Genetic Programming

We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic regression, dCGP offers a new approach to the long unsolved problem of constant representation in GP expressions. On several problems of increasing complexity we find that dCGP is able to find the exact form of the symbolic expression as well as the constants values. We also demonstrate the use of dCGP to solve a large class of differential equations and to find prime integrals of dynamical systems, presenting, in both cases, results that confirm the efficacy of our approach

Cartesian genetic programming for trading: a preliminary investigation

In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches in a number of ways, including being able to evolve programs with limited size and with multiple outputs. CGP is used to evolve a predictor for intraday price movements, and trading strategies using the evolved predictors are evaluated along three dimensions (return, maximum drawdown and recovery factor) and against four different financial datasets (the Euro/US dollar exchange rate and the Dow Jones Industrial Average during periods from 2006 and 2010). We show that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns

Cartesian Genetic Programming in Python

Kartézské genetické programování (CGP) patří mezi evoluční algoritmy. Byl primárně vytvořen pro návrh kombinačních obvodů. Dále může být použit k optimalizaci funkcí, v klasifikaci, evolučním umění atd. Tato práce se zabývá akceleračními technikami urychlující výpočet kandidátního řešení CGP v jazyce Python.Cartesian genetic programming (CGP) is one of the evolutionary methods. It was created for electronic circuit design. It can be used also in optimization of functions, classification, evolutionary art etc. This paper describes acceleration techniques to speed up the evaluation of candidate solution in CGP in Python.

Co-Learning in Cartesian Genetic Programming

Tato práce se zabývá integrací souběžného učení do kartézského genetického programování. Úlohu symbolické regrese se již povedlo vyřešit kartézským genetickým programováním, ovšem tato metoda není dokonalá. Je totiž relativně pomalá a při některých úlohách má tendenci nenalézat požadované řešení. Ale se souběžným učením lze vylepšit některé z~těchto vlastností. V této práci je představena plasticita genotypu, která je založena na Baldwinově efektu. Tento přístup umožňuje jedinci změnit jeho fenotyp během generace. Souběžné učení bylo testováno na pěti rozdílných úlohách pro symbolickou regresi. V experimentech se ukázalo, že pomocí souběžného učení lze dosáhnout až 15násobného urychlení evoluce oproti standardnímu kartézskému genetickému programování bez učení.This thesis deals with the integration of co-learning into cartesian genetic programming. The task of symbolic regression was already solved by cartesian genetic programming, but this method is not perfect yet. It is relatively slow and for certain tasks it tends not to find the desired result. However with co-learning we can enhance some of these attributes. In this project we introduce a genotype plasticity, which is based on Baldwins effect. This approach allows us to change the phenotype of an individual while generation is running. Co-learning algorithms were tested on five different symbolic regression tasks. The best enhancement delivered in experiments by co-learning was that the speed of finding a result was 15 times faster compared to the algorithm without co-learning.

Utilization of Evolutionary Algorithms in Symbolic Regression Problem

Evoluční techniky jsou neustále se vyvíjející a progresivní část informatiky. Evoluční algoritmy se v praxi používají k řešení mnohých druhů problémů od optimalizace až k plánování. Tato práce se zabývá genetickým a kartézským genetickým programováním, které patří mezi nejčastěji používané algoritmy. Cílem práce je implementovat jednotlivé přístupy a vyhodnotit jejich účinnost v úloze symbolické regrese.Evolutionary algorithms are constantly developing and progressive part of informatics. These algorithms serve to solve many kinds of problems from optimal control to planning. This study discusses genetic and cartesian genetic programming, which belong among the most successful types of evolutionary algorithms. The goal of this work is to develop two aplications of genetic and cartesian genetic programming and evaluate efficiency of these two types of evolutionary algorithms in solving symbolic regression problems.