Incorporating characteristics of human creativity into an evolutionary art algorithm

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 2nd 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

Incorporating characteristics of human creativity into an evolutionary art algorithm (journal article)

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 2nd 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

Exploiting development to enhance the scalability of hardware evolution.

Evolutionary algorithms do not scale well to the large, complex circuit design problems typical of the real world. Although techniques based on traditional design decomposition have been proposed to enhance hardware evolution's scalability, they often rely on traditional domain knowledge that may not be appropriate for evolutionary search and might limit evolution's opportunity to innovate. It has been proposed that reliance on such knowledge can be avoided by introducing a model of biological development to the evolutionary algorithm, but this approach has not yet achieved its potential. Prior demonstrations of how development can enhance scalability used toy problems that are not indicative of evolving hardware. Prior attempts to apply development to hardware evolution have rarely been successful and have never explored its effect on scalability in detail. This thesis demonstrates that development can enhance scalability in hardware evolution, primarily through a statistical comparison of hardware evolution's performance with and without development using circuit design problems of various sizes. This is reinforced by proposing and demonstrating three key mechanisms that development uses to enhance scalability: the creation of modules, the reuse of modules, and the discovery of design abstractions. The thesis includes several minor contributions: hardware is evolved using a common reconfigurable architecture at a lower level of abstraction than reported elsewhere. It is shown that this can allow evolution to exploit the architecture more efficiently and perhaps search more effectively. Also the benefits of several features of developmental models are explored through the biases they impose on the evolutionary search. Features that are explored include the type of environmental context development uses and the constraints on symmetry and information transmission they impose, genetic operators that may improve the robustness of gene networks, and how development is mapped to hardware. Also performance is compared against contemporary developmental models

Evolving Graphs by Graph Programming

Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs. This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness. Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search

Reconsideration and extension of Cartesian genetic programming

This dissertation aims on analyzing fundamental concepts and dogmas of a graph-based genetic programming approach called Cartesian Genetic Programming (CGP) and introduces advanced genetic operators for CGP. The results of the experiments presented in this thesis lead to more knowledge about the algorithmic use of CGP and its underlying working mechanisms. CGP has been mostly used with a parametrization pattern, which has been prematurely generalized as the most efficient pattern for standard CGP and its variants. Several parametrization patterns are evaluated with more detailed and comprehensive experiments by using meta-optimization. This thesis also presents a first runtime analysis of CGP. The time complexity of a simple (1+1)-CGP algorithm is analyzed with a simple mathematical problem and a simple Boolean function problem. In the subfield of genetic operators for CGP, new recombination and mutation techniques that work on a phenotypic level are presented. The effectiveness of these operators is demonstrated on a widespread set of popular benchmark problems. Especially the role of recombination can be seen as a big open question in the field of CGP, since the lack of an effective recombination operator limits CGP to mutation-only use. Phenotypic exploration analysis is used to analyze the effects caused by the presented operators. This type of analysis also leads to new insights into the search behavior of CGP in continuous and discrete fitness spaces. Overall, the outcome of this thesis leads to a reconsideration of how CGP is effectively used and extends its adaption from Darwin's and Lamarck's theories of biological evolution

Acceleration of Transistor-Level Evolutionary Design of Digital Circuits Using Zynq

Cílem této práce je návrh a realizace hardwarové jednotky umožňující automatickou syntézu integrovaných obvodů na úrovni tranzistorů. Práce je rozdělena na dvě části. První, teoretická část, se věnuje metodám návrhu obvodů s MOSFET tranzistory a problematice evolučních algoritmů. Dále rozebírá aktuální výsledky výzkumu v této oblasti a navazuje popisem nového přístupu evolučního návrhu a optimalizace číslicových obvodů na úrovni tranzistorů. Následující část se zabývá popisem hardwarové jednotky, která tuto novou metodu akceleruje na obvodu Zynq integrující procesor ARM a programovatelnou logiku. Funkčnost metody je prezentována na optimalizaci vícevstupých obvodů. Hardwarová jednotka byla využita v evolučním návrhu dvou a třívstupých hradel.The goal of this project is to design a hardware unit that is designed to accelerate evolutionary design of digital circuits on transistor level. The project is divided to two parts. The first one describes design methods of the MOSFET circuits and issues of evolutionary algorithms. It also analyses current results in this domain and provides a new method for the design and optimization. The second part describes proposed unit that accelerates the new method on the circuit Zynq which integrates ARM processor and programmable logic. The new method functionality has been empirically analysed in the task of optimization of few circuits with more inputs. The hardware unit has been tested for designing of gates on transistor level.

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp