Mutation Operators for the Evolution of Finite Automata

Evolutionary programming has originally been proposed for the breeding of finite state automata. The mutation operator is working directly on the graph structure of the automata. In this paper we introduce variation operators based on the automatons input/output behavior rather than its structure. The operators are designed to make use of additional information based on a ranking of states as well as a problem-specific metric which enhances the search process

Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds

Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results.  This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model

Super-structure and super-structure free design search space representations for a building spatial design in multi-disciplinary building optimisation

In multi-disciplinary building optimisation, solutions depend on the representation of the design search space, the latter being a collection of all solutions. This paper presents two design search space representations and discusses their advantages and disadvantages: The first, a super-structure approach, requires all possible solutions to be prescribed in a so-called super-structure. The second approach, super-structure free, uses dynamic data structures that offer freedom in the range of possible solutions. It is concluded that both approaches may supplement each other, if applied in a combination of optimisation methods. A method for this combination of optimisation methods is proposed. The method includes the transformation of one representation into the other and vice versa. Finally, therefore in this paper these transformations are proposed, implemented, and verified as well.Algorithms and the Foundations of Software technolog

Toolbox for super-structured and super-structure free multi-disciplinary building spatial design optimisation

Multi-disciplinary optimisation of building spatial designs is characterised by large solution spaces. Here two approaches are introduced, one being super-structured and the other super-structure free. Both are different in nature and perform differently for large solution spaces and each requires its own representation of a building spatial design, which are also presented here. A method to combine the two approaches is proposed, because the two are prospected to supplement each other. Accordingly a toolbox is presented, which can evaluate the structural and thermal performances of a building spatial design to provide a user with the means to define optimisation procedures. A demonstration of the toolbox is given where the toolbox has been used for an elementary implementation of a simulation of co-evolutionary design processes. The optimisation approaches and the toolbox that are presented in this paper will be used in future efforts for research into- and development of optimisation methods for multi-disciplinary building spatial design optimisation

Toward a unifying framework for evolutionary processes

The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields

Unifying a Geometric Framework of Evolutionary Algorithms and Elementary Landscapes Theory

Evolutionary algorithms (EAs) are randomised general-purpose strategies, inspired by natural evolution, often used for finding (near) optimal solutions to problems in combinatorial optimisation. Over the last 50 years, many theoretical approaches in evolutionary computation have been developed to analyse the performance of EAs, design EAs or measure problem difficulty via fitness landscape analysis. An open challenge is to formally explain why a general class of EAs perform better, or worse, than others on a class of combinatorial problems across representations. However, the lack of a general unified theory of EAs and fitness landscapes, across problems and representations, makes it harder to characterise pairs of general classes of EAs and combinatorial problems where good performance can be guaranteed provably. This thesis explores a unification between a geometric framework of EAs and elementary landscapes theory, not tied to a specific representation nor problem, with complementary strengths in the analysis of population-based EAs and combinatorial landscapes. This unification organises around three essential aspects: search space structure induced by crossovers, search behaviour of population-based EAs and structure of fitness landscapes. First, this thesis builds a crossover classification to systematically compare crossovers in the geometric framework and elementary landscapes theory, revealing a shared general subclass of crossovers: geometric recombination P-structures, which covers well-known crossovers. The crossover classification is then extended to a general framework for axiomatically analysing the population behaviour induced by crossover classes on associated EAs. This shows the shared general class of all EAs using geometric recombination P-structures, but no mutation, always do the same abstract form of convex evolutionary search. Finally, this thesis characterises a class of globally convex combinatorial landscapes shared by the geometric framework and elementary landscapes theory: abstract convex elementary landscapes. It is formally explained why geometric recombination P-structure EAs expectedly can outperform random search on abstract convex elementary landscapes related to low-order graph Laplacian eigenvalues. Altogether, this thesis paves a way towards a general unified theory of EAs and combinatorial fitness landscapes

A modular genetic programming system

Genetic Programming (GP) is an evolutionary algorithm for the automatic discovery of symbolic expressions, e.g. computer programs or mathematical formulae, that encode solutions to a user-defined task. Recent advances in GP systems and computer performance made it possible to successfully apply this algorithm to real-world applications. This work offers three main contributions to the state-of-the art in GP systems: (I) The documentation of RGP, a state-of-the art GP software implemented as an extension package to the popular R environment for statistical computation and graphics. GP and RPG are introduced both formally and with a series of tutorial examples. As R itself, RGP is available under an open source license. (II) A comprehensive empirical analysis of modern GP heuristics based on the methodology of Sequential Parameter Optimization. The effects and interactions of the most important GP algorithm parameters are analyzed and recommendations for good parameter settings are given. (III) Two extensive case studies based on real-world industrial applications. The first application involves process control models in steel production, while the second is about meta-model-based optimization of cyclone dust separators. A comparison with traditional and modern regression methods reveals that GP offers equal or superior performance in both applications, with the additional benefit of understandable and easy to deploy models. Main motivation of this work is the advancement of GP in real-world application areas. The focus lies on a subset of application areas that are known to be practical for GP, first of all symbolic regression and classification. It has been written with practitioners from academia and industry in mind

Quality-driven Multi-objective Optimization of Software Architecture Design: Method, Tool, and Application

Software architecting is a non-trivial and demanding task for software engineers to perform. The architecture is a key enabler for software systems. Besides being crucial for user functionality, the software architecture has deep impact on software qualities such as performance, safety, and cost. In this dissertation, an automated approach for software architecture design is proposed that supports analysis and optimization of multiple quality attributes:First of all, we demonstrate an optimization approach for automated software architecture design. It reports the results of applying our architecture optimization framework to an automotive sub-system that was conducted based on a large-scale real world case study. Moreover, we introduce two novel degrees of freedom which demonstrate how the number of processing nodes and their interconnecting network can be codified to fit into a genetic algorithm. Our studies show that these extra degrees of freedom lead to better overall software architecture optimization. Finally, we propose a new search-based approach for generating a set of optimal software architectural solutions for use in software product lines. Our new approach analyses the commonality of the found optimal solutions and proposes a set of solutions which are suitable for the range of products defined by various feature combinations.Algorithms and the Foundations of Software technolog

Uncertainty handling in surrogate assisted optimisation of games

In this thesis entitled Uncertainty handling in surrogate assisted optimisation of games, we started out with the goal to investigate the uncertainty in game optimisation problems, as well as to identify or develop suitable optimisation algorithms. In order to approach this problem systematically, we first created a benchmark consisting of suitable game optimisation functions (GBEA). The suitability of these functions was determined using a taxonomy that was created based on the results of a literature survey of automatic game evaluation approaches. In order to improve the interpretability of the results, we also implemented an experimental framework that adds several features aiding the analysis of the results, specifically for surrogate-assisted evolutionary algorithms. After describing potentially suitable algorithms, we proposed a promising algorithm (SAPEO), to be tested on the benchmark alongside state-of-the-art optimisation algorithms. SAPEO is utilising the observation that most evolutionary algorithms only need fitness evaluations for survival selections. However, if the individuals in a population can be distinguished reliably based on predicted values, the number of function evaluations can be reduced. After a theoretical analysis of the performance limits of SAPEO, which produced very promising insights, we conducted several sets of experiments in order to answer the three central hypotheses guiding this thesis. We find that SAPEO performs comparably to state-of-the-art surrogate-assisted algorithms, but all are frequently outperformed by stand-alone evolutionary algorithms. From a more detailed analysis of the behaviour of SAPEO, we identify a few pointers that could help to further improve the performance. Before running experiments on the developed benchmark, we first verify its suitability using a second set of experiments. We find that GBEA is practical and contains interesting and challenging functions. However, we also discover that, in order to produce interpretable result with the benchmark, a set of baseline results is required. Due to this issue, we are not able to produce meaningful results with the GBEA at the time of writing. However, after more experiments are conducted with the benchmark, we will be able to interpret our results in the future. The insights developed will most likely not only be able to provide an assessment of optimisation algorithms, but can also be used to gain a deeper understanding of the characteristics of game optimisation problems