Event-based graphical monitoring in the EpochX genetic programming framework

EpochX is a genetic programming framework with provision for event management – similar to the Java event model – allowing the notification of particular actions during the lifecycle of the evolutionary algorithm. It also provides a flexible Stats system to gather statistics measures. This paper introduces a graphical interface to the EpochX genetic programming framework, taking full advantage of EpochX's event management. A set of representation-independent and tree-dependent GUI components are presented, showing how statistic information can be presented in a rich format using the information provided by EpochX's Stats system

BOSS: Bayesian Optimization over String Spaces

This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. Recent applications of BO over strings have been hindered by the need to map inputs into a smooth and unconstrained latent space. Learning this projection is computationally and data-intensive. Our approach instead builds a powerful Gaussian process surrogate model based on string kernels, naturally supporting variable length inputs, and performs efficient acquisition function maximization for spaces with syntactical constraints. Experiments demonstrate considerably improved optimization over existing approaches across a broad range of constraints, including the popular setting where syntax is governed by a context-free grammar

Multi-level diversity promotion strategies for Grammar-guided Genetic Programming

Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype-phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyses in diversity promotion

CSM-430: Geometric Landscape of Homologous Crossover for Syntactic Trees

Geometric crossover and geometric mutation are representation-independent operators that are welldefined once a notion of distance over the solution space is defined. They were obtained as generalizations of genetic operators for binary strings and real vectors. Our geometric framework has been successfully applied to the permutation representation leading to a clarification and a natural unification of this domain. The relationship between search space, distances and genetic operators for syntactic trees is little understood. In this paper we apply the geometric framework to the syntactic tree representation and show how the wellknown structural distance is naturally associated with homologous crossover and subtree mutation

Evolving Source Code: Object Oriented Genetic Programming in .NET Core

Abstract. Object Oriented Genetic Programming (OOGP) is a method of Genetic Programming (GP) which gives access to standard language libraries, iteration and object-oriented method calls. The implementation of OOGP in this paper shows the automatic generation of retrievable C# files, following standard C# coding conventions with potential access to the entire C# library, derived from a genetic sequence. This new implementation utilises .net Core Roslyn, using reflection, which allows for retrievable, runtime execution and unloading of dynamically generated C# files with scope control in a modern server environment. Experiments were performed on unit tests to validate the algorithms ability to solve simple programming tasks and generate functional, plain text code. This is a new prototype designed to eventually act as the main Artificial Intelligence controller for a novel, behaviourally adaptive, Artificial-Life simulation. The design taken in the development of this algorithm stems from a requirement for a high potential variation in behaviour, processing efficiency in a server environment per iteration through generated code and low a minimal number of generations

Epigenetic Tracking: Implementation Details

"Epigenetic Tracking" is the name of a model of cellular development that, coupled with an evolutionary technique, becomes an evo-devo (or "artificial embryology", or "computational development") method to generate 2d or 3d sets of artificial cells arbitrarily shaped. 'In silico' experiments have proved the effectiveness of the method in devo-evolving any kind of shape, of any complexity (in terms of number of cells, number of colours, etc.); being shape complexity a metaphor for organismal complexity, such simulations established its potential to generate the complexity typical of biological systems. Moreover, it has also been shown how the underlying model of cellular development is able to produce the artificial version of key biological phenomena such as embryogenesis, the presence of "junk DNA", the phenomenon of ageing and the process of carcinogenesis. The objective of this document is not to provide new material (most of the material presented here has already been published elsewhere): rather, it is to provide all details that, for lack of space, could not be provided in the published papers and in particular to give all technical details necessary to re-implement the method.Comment: 26 pages, 20 figure

Weighted Hierarchical Grammatical Evolution

Grammatical evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings, of a language defined by a user-provided context-free grammar. In this paper, we propose a novel procedure for mapping genotypes to phenotypes that we call weighted hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results of the standard GE framework as well as two of the most significant enhancements proposed in the literature: 1) position-independent GE and 2) structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure