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Incorporating Characteristics of Human Creativity into an Evolutionary Art Algorithm
"... Figure 1. Source Darwin image with examples of evolved abstract portraits created using an automatic creative system. A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generatio ..."
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Cited by 21 (7 self)
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Figure 1. Source Darwin image with examples of evolved abstract portraits created using an automatic creative system. A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2 nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.
Embedded cartesian genetic programming and the lawnmower and hierarchical-if-and-only-if problems
- In Proc. of GECCO. ACM
, 2006
"... Embedded Cartesian Genetic Programming (ECGP) is an extension of the directed graph based Cartesian Genetic Programming (CGP), which is capable of automatically acquiring, evolving and re-using partial solutions in the form of modules. In this paper, we apply for the first time, CGP and ECGP to the ..."
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Cited by 6 (5 self)
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Embedded Cartesian Genetic Programming (ECGP) is an extension of the directed graph based Cartesian Genetic Programming (CGP), which is capable of automatically acquiring, evolving and re-using partial solutions in the form of modules. In this paper, we apply for the first time, CGP and ECGP to the well known Lawnmower problem and to the Hierarchical-if-and-Only-if problem. The latter is normally associated with Genetic Algorithms. Computational effort figures are calculated from the results of both CGP and ECGP and our results compare favourably with other techniques.
MOVES: A Modular Framework for Hardware Evolution
- In Proceedings of the NASA/ESA Conference on Adaptive Hardware and Systems, IEEE Computer Society Press (2007) 447-454 Kaufmann P., Platzner M. Toward
"... In this paper, we present a framework that supports ex-perimenting with evolutionary hardware design. We de-scribe the framework’s modules for composing evolutionary optimizers and for setting up, controlling, and analyzing ex-periments. Two case studies demonstrate the usefulness of the framework: ..."
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Cited by 3 (1 self)
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In this paper, we present a framework that supports ex-perimenting with evolutionary hardware design. We de-scribe the framework’s modules for composing evolutionary optimizers and for setting up, controlling, and analyzing ex-periments. Two case studies demonstrate the usefulness of the framework: evolution of hash functions and evolution based on pre-engineered circuits. 1
Advanced Techniques for the Creation and Propagation of Modules in Cartesian Genetic Programming
"... The choice of an appropriate hardware representation model is key to successful evolution of digital circuits. One of the most popular models is cartesian genetic programming, which encodes an array of logic gates into a chromosome. While several smaller circuits have been successfully evolved on th ..."
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The choice of an appropriate hardware representation model is key to successful evolution of digital circuits. One of the most popular models is cartesian genetic programming, which encodes an array of logic gates into a chromosome. While several smaller circuits have been successfully evolved on this model, it lacks scalability. A recent approach towards scalable hardware evolution is based on the automated creation of modules from primitive gates. In this paper, we present two novel approaches for module creation, an age-based and a cone-based technique. Further, we detail a cone-based crossover operator for use with cartesian genetic programming. We evaluate the different techniques and compare them with related work. The results show that age-based module creation is highly effective, while cone-based approaches are only beneficial for regularly structured, multiple output functions such as multipliers.
Innovative Batik Design with an Interactive Evolutionary Art System
, 2009
"... Abstract This paper describes an evolutionary art system, which explores the potential ability of evolutionary computation in Batik design. We investigate the use of Interactive Evolutionary Algorithm (IEA) in our system, with the goal of enhancing user’s creativity to generate innovative Batik-like ..."
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Abstract This paper describes an evolutionary art system, which explores the potential ability of evolutionary computation in Batik design. We investigate the use of Interactive Evolutionary Algorithm (IEA) in our system, with the goal of enhancing user’s creativity to generate innovative Batik-like patterns. We focus mainly on two crucial aspects of the system. First, a new representation is proposed to capture the features in Batik and create innovative patterns through evolutionary processes. Second, an out-breeding mechanism is applied to our system, in order to sustain user’s interest for a longer period. Our system can search a much larger design space than other systems and can avoid being trapped in a local optimum. We describe the system in detail and the methodology we have adopted in the system. Our experimental results have shown that our newly developed system is effective and has great potentials in evolving novel Batik design.
Genetic Programming Track
"... Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) that can automatically acquire, evolve and re-use partial solutions in the form of modules. In this paper, we introduce for the first time a new multi-chromosome approach to CGP and ECGP that allows ..."
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Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) that can automatically acquire, evolve and re-use partial solutions in the form of modules. In this paper, we introduce for the first time a new multi-chromosome approach to CGP and ECGP that allows difficult problems with multiple outputs to be broken down into many smaller, simpler problems with single outputs, whilst still encoding the entire solution in a single genotype. We also propose a multi-chromosome evolutionary strategy which selects the best chromosomes from the entire population to form the new fittest individual, which may not have been present in the population. The multi-chromosome approach to CGP and ECGP is tested on a number of multiple output digital circuits. Computational Effort figures are calculated for each problem and compared against those for CGP and ECGP. The results indicate that the use of multiple chromosomes in both CGP and ECGP provide a significant performance increase on all problems tested.
ARCHITECTURES AND TECHNOLOGIES Deliverable D1.1.1
"... future architectures, implementation Abstract: This report deals with the progresses done during the first year of the ÆTHER project concerning the hardware sub-project. The major objective for sub-project 1 that is addressed by this document is the proposal of a suitable hardware architecture based ..."
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future architectures, implementation Abstract: This report deals with the progresses done during the first year of the ÆTHER project concerning the hardware sub-project. The major objective for sub-project 1 that is addressed by this document is the proposal of a suitable hardware architecture based on “on the fly ” reconfigurable hardware. For this purpose, we define self-adaptation from the hardware point of view in order to extract some requirements related to a self-adaptive computing architecture. This preliminary phase leads us to propose a basic computing unit which has the ability to change its behaviour and its operations in order to react to some events. This self-adaptive computing element called SANE (for Self-Adaptive Networked Entity) is intended to work within a network made up of several similar units that cooperate with each other to form complex but manageable systems. The relevance of the high-reconfiguration rate property is discussed as well as some implementation tracks studied by SP1 partners. These various implementations range from reconfigurable hardware architectures such as advanced FPGA structures to more unconventional approaches. The SANE concept is then illustrated by some
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 The Automatic Acquisition, Evolution and Reuse of Modules in C
"... Abstract—This paper presents a generalization of the graph-based genetic programming (GP) technique known as Cartesian genetic programming (CGP). We have extended CGP by utilizing automatic module acquisition, evolution, and reuse. To benchmark the new technique, we have tested it on: various digita ..."
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Abstract—This paper presents a generalization of the graph-based genetic programming (GP) technique known as Cartesian genetic programming (CGP). We have extended CGP by utilizing automatic module acquisition, evolution, and reuse. To benchmark the new technique, we have tested it on: various digital circuit problems, two symbolic regression problems, the lawnmower problem, and the hierarchical if-and-only-if problem. The results show the new modular method evolves solutions quicker than the original nonmodular method, and the speedup is more pronounced on larger problems. Also, the new modular method performs fa-vorably when compared with other GP methods. Analysis of the evolved modules shows they often produce recognizable functions. Prospects for further improvements to the method are discussed. Index Terms—Automatically defined functions (ADFs), Carte-sian genetic programming (CGP), embedded Cartesian genetic programming (ECGP), genetic programming (GP), graph-based representations, modularity, module acquisition. I.
PPSN 2014 Tutorial: Cartesian Genetic Programming
"... Evolved pictureEvolved picture Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming. Cartesian Genetic Programming is a highly cited technique that was developed by Julian Miller in 1999 and 2000 from some earlier joint work of Julian Miller with P ..."
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Evolved pictureEvolved picture Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming. Cartesian Genetic Programming is a highly cited technique that was developed by Julian Miller in 1999 and 2000 from some earlier joint work of Julian Miller with Peter Thomson in 1997. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. Since then, the classical form of CGP has been developed made more efficient in various ways. Notably by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the
GECCO 2012 Tutorial: Cartesian Genetic Programming
, 2012
"... Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are ver ..."
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Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. Since then, the classical form of CGP has been developed made more efficient in various ways. Notably, by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.
