32 research outputs found
The Case for a Mixed-Initiative Collaborative Neuroevolution Approach
It is clear that the current attempts at using algorithms to create
artificial neural networks have had mixed success at best when it comes to
creating large networks and/or complex behavior. This should not be unexpected,
as creating an artificial brain is essentially a design problem. Human design
ingenuity still surpasses computational design for most tasks in most domains,
including architecture, game design, and authoring literary fiction. This leads
us to ask which the best way is to combine human and machine design capacities
when it comes to designing artificial brains. Both of them have their strengths
and weaknesses; for example, humans are much too slow to manually specify
thousands of neurons, let alone the billions of neurons that go into a human
brain, but on the other hand they can rely on a vast repository of common-sense
understanding and design heuristics that can help them perform a much better
guided search in design space than an algorithm. Therefore, in this paper we
argue for a mixed-initiative approach for collaborative online brain building
and present first results towards this goal.Comment: Presented at WebAL-1: Workshop on Artificial Life and the Web 2014
(arXiv:1406.2507
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
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
DrawCompileEvolve : sparking interactive evolutionary art with human creations.
This paper presents DrawCompileEvolve, a web-based drawing tool which allows users to draw simple primitive shapes, group them together or define patterns in their groupings (e.g. symmetry, repetition). The user’s vector drawing is then compiled into an indirectly encoded genetic representation, which can be evolved interactively, allowing the user to change the image’s colors, patterns and ultimately transform it. The human artist has direct control while drawing the initial seed of an evolutionary run and indirect control while interactively evolving it, thus making DrawCompileEvolve a mixed-initiative art tool. Early results in this paper show the potential of DrawCompileEvolve to jump-start evolutionary art with meaningful drawings as well as the power of the underlying genetic representation to transform the user’s initial drawing into a different, yet potentially meaningful, artistic rendering.peer-reviewe
Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space
For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others\u27 images. Through this process of branching from other images, and through continually increasing image complexity made possible by the underlying neuroevolution of augmenting topologies (NEAT) algorithm, evolved images proliferate unlike in any other current IEC system. This paper discusses not only the strengths of the Picbreeder approach, but its challenges and shortcomings as well, in the hope that lessons learned will inform the design of future CIE systems