Towards identifying salient patterns in genetic programming individuals

This thesis addresses the problem of offline identification of salient patterns in genetic programming individuals. It discusses the main issues related to automatic pattern identification systems, namely that these (a) should help in understanding the final solutions of the evolutionary run, (b) should give insight into the course of evolution and (c) should be helpful in optimizing future runs. Moreover, it proposes an algorithm, Extended Pattern Growing Algorithm ([E]PGA) to extract, filter and sort the identified patterns so that these fulfill as many as possible of the following criteria: (a) they are representative for the evolutionary run and/or search space, (b) they are human-friendly and (c) their numbers are within reasonable limits. The results are demonstrated on six problems from different domains.

Evolution of lace knitting stitch patterns by genetic programming

In this paper we study the generation of lace knitting stitch patterns by using genetic programming. We devise a genetic representation of knitting charts that accurately reflects their usage for hand knitting the pattern. We apply a basic evolutionary algorithm for generating the patterns, where the key of success is evaluation. We propose automatic evaluation of the patterns, without interaction with the user. We present some patterns generated by the method and then discuss further possibilities for bringing automatic evaluation closer to human evaluation. Copyright 2007 ACM.

Modelling human preference in evolutionary art

Creative activities including arts are characteristic to humankind. Our understanding of creativity is limited, yet there is substantial research trying to mimic human creativity in artificial systems and in particular to produce systems that automatically evolve art appreciated by humans. We propose here to model human visual preference by a set of aesthetic measures identified through observation of human selection of images and then use these for automatic evolution of aesthetic images. © 2011 Springer-Verlag.

Modelling the underlying principles of human aesthetic preference in evolutionary art

Our understanding of creativity is limited, yet there is substantial research trying to mimic human creativity in artificial systems and in particular to produce systems that automatically evolve art appreciated by humans. We propose here to study human visual preference through observation of nearly 500 user sessions with a simple evolutionary art system. The progress of a set of aesthetic measures throughout each interactive user session is monitored and subsequently mimicked by automatic evolution in an attempt to produce an image to the liking of the human user.

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. © 2013 Springer Science+Business Media New York.

Using genetic programming and decision trees for generating structural descriptions of four bar mechanisms

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

Fitness and novelty in evolutionary art

In this paper the effects of introducing novelty search in evolutionary art are explored. Our algorithm combines fitness and novelty metrics to frame image evolution as a multi-objective optimisation problem, promoting the creation of images that are both suitable and diverse. The method is illustrated by using two evolutionary art engines for the evolution of figurative objects and context free design grammars. The results demonstrate the ability of the algorithm to obtain a larger set of fit images compared to traditional fitness-based evolution, regardless of the engine used.

Evolving ambiguous images

This work explores the creation of ambiguous images, i.e., images that may induce multistable perception, by evolutionary means. Ambiguous images are created using a general purpose approach, composed of an expression-based evolutionary engine and a set of object detectors, which are trained in advance using Machine Learning techniques. Images are evolved using Genetic Programming and object detectors are used to classify them. The information gathered during classification is used to assign fitness. In a first stage, the system is used to evolve images that resemble a single object. In a second stage, the discovery of ambiguous images is promoted by combining pairs of object detectors. The analysis of the results highlights the ability of the system to evolve ambiguous images and the differences between computational and human ambiguous images.

Parallel Problem Solving from Nature – PPSN XIV:14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.

A critical evaluation of theories of nationalism

This thesis considers the main theoretical positions within the contemporary sociology of nationalism. These can be grouped into two basic types, primordialist theories which assert that nationalism is an inevitable aspect of all human societies, and modernist theories which assert that nationalism and the nation-state first developed within western Europe in recent centuries. With respect to primordialist approaches to nationalism, it is argued that the main common explanation offered is human biological propensity. Consideration is concentrated on the most recent and plausible of such theories, sociobiology. Sociobiological accounts root nationalism and racism in genetic programming which favours close kin, or rather to the redirection of this programming in complex societies, where the social group is not a kin group. It is argued that the stated assumptions of the sociobiologists do not entail the conclusions they draw as to the roots of nationalism, and that in order to arrive at such conclusions further and implausible assumptions have to be made. With respect to modernists, the first group of writers who are considered are those, represented by Carlton Hayes, Hans Kohn and Elie Kedourie, whose main thesis is that the nation-state and nationalism are recent phenomena. Next, the two major attempts to relate nationalism and the nation-state to imperatives specific either to capitalist societies (in the `orthodox' marxist theory elaborated about the turn of the twentieth century) or to the processes of modernisation and industrialisation (the `Weberian' account of Ernest Gellner) are discussed. It is argued that modernist accounts can only be sustained by starting from a definition of nationalism and the nation-state which conflates such phenomena with others which are specific to the modern world. The marxist and Gellner accounts form the necessary starting point for any explanation as to why the nation-state is apparently the sole viable form of polity in the modern world, but their assumption that no pre-modern society was national leaves them without an adequate account of the earliest origins of the nation-state and of nationalism. Finally, a case study from the history of England argues both the achievement of a national state form and the elucidation of crucial components of a nationalist ideology were attained at a period not consistent with any of the versions of the modernist thesis.

A data structure for improved gp analysis via efficient computation and visualisation of population measures

Population measures for genetic programs are defined and analysed in an attempt to better understand the behaviour of genetic programming. Some measures are simple, but do not provide sufficient insight. The more meaningful ones are complex and take extra computation time. Here we present a unified view on the computation of population measures through an information hypertree (iTree). The iTree allows for a unified and efficient calculation of population measures via a basic tree traversal. © Springer-Verlag 2004.

Stability analysis of tree structured decision functions

In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies. © 2003 Elsevier B.V. All rights reserved.

Evolutionary neurobiology and art

In his important book on evolutionary theory, Darwin's Dangerous Idea, Daniel Dennett warns that Darwin's idea seeps through every area of human discourse like a "universal acid" (Dennett, 1995). Art and the aesthetic response cannot escape its influence. So my approach in this chapter is essentially naturalistic. Friedrich Nietzsche writes of observing the human comedy from afar, "like a cold angel...without anger, but without warmth" (Nietzsche, 1872, p. 164). Whether Nietzsche, of all people, could have done this is a matter of debate. But we know what he means. It describes a stance outside the human world as if looking down on human folly from Mount Olympus. From this stance, humans, their art and neurology are all part of the natural world, all part of the evolutionary process, the struggle for existence. The anthropologist David Dutton, in his contribution to the Routledge Companion to Aesthetics, says that all humans have an aesthetic sense (Dutton, 2001). It is a human universal. Biologists argue that such universals have an evolutionary basis. Furthermore, many have argued that not only humans but also animals, at least the higher mammals and birds, have an appreciation of the beautiful and the ugly (Eibl-Eibesfeldt, 1988).11Charles Darwin indeed writes "Birds appear to be the most aesthetic of all animals, excepting, of course, man, and they have nearly the same sense of the beautiful that we have" (1871, The Descent of Man and Selection in Relation to Sex, London: John Murray, vol.2, xiii, 39). This again suggests that aesthetics has an evolutionary origin. In parenthesis here, I should perhaps say that I am well aware of the criticism leveled at evolutionary psychology. I am well aware that it has been attacked as just so many "just-so" stories. This is neither the time nor the place to mount a defense but simply just to say that I believe that a defense is eminently feasible. © 2006 Elsevier Inc. All rights reserved.

PCB assembly line assignment:a genetic algorithm approach

Purpose – This paper sets out to study a production-planning problem for printed circuit board (PCB) assembly. A PCB assembly company may have a number of assembly lines for production of several product types in large volume. Design/methodology/approach – Pure integer linear programming models are formulated for assigning the product types to assembly lines, which is the line assignment problem, with the objective of minimizing the total production cost. In this approach, unrealistic assignment, which was suffered by previous researchers, is avoided by incorporating several constraints into the model. In this paper, a genetic algorithm is developed to solve the line assignment problem. Findings – The procedure of the genetic algorithm to the problem and a numerical example for illustrating the models are provided. It is also proved that the algorithm is effective and efficient in dealing with the problem. Originality/value – This paper studies the line assignment problem arising in a PCB manufacturing company in which the production volume is high.

A genetic algorithm for the generalised transportation problem

The generalised transportation problem (GTP) is an extension of the linear Hitchcock transportation problem. However, it does not have the unimodularity property, which means the linear programming solution (like the simplex method) cannot guarantee to be integer. This is a major difference between the GTP and the Hitchcock transportation problem. Although some special algorithms, such as the generalised stepping-stone method, have been developed, but they are based on the linear programming model and the integer solution requirement of the GTP is relaxed. This paper proposes a genetic algorithm (GA) to solve the GTP and a numerical example is presented to show the algorithm and its efficiency.