Semantically-based crossover in genetic programming: application to real-valued symbolic regression

We investigate the effects of semantically-based crossover operators in genetic programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as semantic equivalence and semantic similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operators—semantics aware crossover (SAC) and semantic similarity-based crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with standard crossover (SC), context aware crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC

Hoare Logic-based Genetic Programming

Almost all existing genetic programming systems deal with fitness evaluation solely by testing. In this paper, by contrast, we present an original approach that combines genetic programming with Hoare logic with the aid of model checking and finite state automata, henceby proposing a brand new verification-focused formal genetic programming system that makes it possible to evolve reliable programs with mathematically-verified properties

Synthesis of Probabilistic Models for Quality-of-Service Software Engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability

Ensemble learning with GSGP

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe purpose of this thesis is to conduct comparative research between Genetic Programming (GP) and Geometric Semantic Genetic Programming (GSGP), with different initialization (RHH and EDDA) and selection (Tournament and Epsilon-Lexicase) strategies, in the context of a model-ensemble in order to solve regression optimization problems. A model-ensemble is a combination of base learners used in different ways to solve a problem. The most common ensemble is the mean, where the base learners are combined in a linear fashion, all having the same weights. However, more sophisticated ensembles can be inferred, providing higher generalization ability. GSGP is a variant of GP using different genetic operators. No previous research has been conducted to see if GSGP can perform better than GP in model-ensemble learning. The evolutionary process of GP and GSGP should allow us to learn about the strength of each of those base models to provide a more accurate and robust solution. The base-models used for this analysis were Linear Regression, Random Forest, Support Vector Machine and Multi-Layer Perceptron. This analysis has been conducted using 7 different optimization problems and 4 real-world datasets. The results obtained with GSGP are statistically significantly better than GP for most cases.O objetivo desta tese é realizar pesquisas comparativas entre Programação Genética (GP) e Programação Genética Semântica Geométrica (GSGP), com diferentes estratégias de inicialização (RHH e EDDA) e seleção (Tournament e Epsilon-Lexicase), no contexto de um conjunto de modelos, a fim de resolver problemas de otimização de regressão. Um conjunto de modelos é uma combinação de alunos de base usados de diferentes maneiras para resolver um problema. O conjunto mais comum é a média, na qual os alunos da base são combinados de maneira linear, todos com os mesmos pesos. No entanto, conjuntos mais sofisticados podem ser inferidos, proporcionando maior capacidade de generalização. O GSGP é uma variante do GP usando diferentes operadores genéticos. Nenhuma pesquisa anterior foi realizada para verificar se o GSGP pode ter um desempenho melhor que o GP no aprendizado de modelos. O processo evolutivo do GP e GSGP deve permitir-nos aprender sobre a força de cada um desses modelos de base para fornecer uma solução mais precisa e robusta. Os modelos de base utilizados para esta análise foram: Regressão Linear, Floresta Aleatória, Máquina de Vetor de Suporte e Perceptron de Camadas Múltiplas. Essa análise foi realizada usando 7 problemas de otimização diferentes e 4 conjuntos de dados do mundo real. Os resultados obtidos com o GSGP são estatisticamente significativamente melhores que o GP na maioria dos casos

Search-Based Synthesis of Probabilistic Models for Quality-of-Service Software Engineering

The formal verification of finite-state probabilistic models supports the engineering of software with strict quality-of-service (QoS) requirements. However, its use in software design is currently a tedious process of manual multiobjective optimisation. Software designers must build and verify probabilistic models for numerous alternative architectures and instantiations of the system parameters. When successful, they end up with feasible but often suboptimal models. The EvoChecker search-based software engineering approach and tool introduced in our paper employ multiobjective optimisation genetic algorithms to automate this process and considerably improve its outcome. We evaluate EvoChecker for six variants of two software systems from the domains of dynamic power management and foreign exchange trading. These systems are characterised by different types of design parameters and QoS requirements, and their design spaces comprise between 2E+14 and 7.22E+86 relevant alternative designs. Our results provide strong evidence that EvoChecker significantly outperforms the current practice and yields actionable insights for software designers

Genetic Programming with Fitness based on Model Checking

Model checking is a way of analysing programs and program-like structures to decide whether they satisfy a list of temporal logic statements describing desired behaviour. In this paper we apply this to the fitness checking stage in an evolution strategy for learning finite state machines. We give experimental results consisting of learning the control program for a vending machine