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

A Compelling Overview of Art Therapy Techniques and Outcomes

Art Therapy Has Many Faces is an enlightening film that richly illustrates the significance and impact of art as a therapeutic tool in human lives. As the film states, there is a “magic power of the image” that serves to reaffirm the age-old saying that “a picture is worth a thousand words.” Indeed, one becomes convinced that no amount of talk could have unearthed some of the feelings and events portrayed in the art produced in art therapy programs depicted in this film

Perspectives on Artistic Creativity: A Review of ‘The Artful Mind’ (Mark Turner, Ed.)

This book is not actually about the artful mind, i.e. the mind of one who uses devious means to achieve certain ends, but the artistic mind, i.e. the mind of one who creates art. It consists of fourteen chapters by prominent scholars from a variety of disciplines ranging from art history to cognitive science to modern languages and literature. The book is the result of these scholars meeting for periods ranging from a few months to a year at the Center for Advanced Study in the Behavioral Sciences in 2001-2002. It is a provocative and eclectic compilation of perspectives on what goes through the mind of the artist, how the creative process works, and how human creativity came about. This review will say a few words about each chapter, and end with a comment on some recurring themes

Why the Creative Process is Not Darwinian

Simonton (2006) makes the unwarranted assumption that nonmonotonicity supports a Darwinian view of creativity. Darwin’s theory of natural selection was motivated by a paradox that has no equivalent in creative thought: the paradox of how change accumulates when acquired traits are not inherited. To describe a process of cumulative change in which acquired traits are retained is outside of the scope of the theory of natural selection. Even the early evolution of life itself (prior to genetically mediated template replication) cannot be described by natural selection. Specifically, natural selection cannot describe change of state that involves horizontal (Lamarckian) exchange, or occurs through interaction with an incompletely specified context. It cannot describe change wherein variants are evaluated sequentially, and wherein this evaluation can itself change the state space and/or fitness function, because no two variants are ever evaluated according to the same selection criterion. Concerns are also raised as to the methodology used in Simonton’s study

Epigenetic and Cultural Evolution are non-Darwinian

The argument that heritable epigenetic change plays a distinct role in evolution would be strengthened through recognition that it is what bootstrapped the origin and early evolution of life, and like behavioral and symbolic change, is non-Darwinian. The mathematics of natural selection, a population-level process, is limited to replication with negligible individual-level change, i.e. that uses a self-assembly code

A semantic network-based evolutionary algorithm for computational creativity

We introduce a novel evolutionary algorithm (EA) with a semantic network-based representation. For enabling this, we establish new formulations of EA variation operators, crossover and mutation, that we adapt to work on semantic networks. The algorithm employs commonsense reasoning to ensure all operations preserve the meaningfulness of the networks, using ConceptNet and WordNet knowledge bases. The algorithm can be interpreted as a novel memetic algorithm (MA), given that (1) individuals represent pieces of information that undergo evolution, as in the original sense of memetics as it was introduced by Dawkins; and (2) this is different from existing MA, where the word “memetic” has been used as a synonym for local refinement after global optimization. For evaluating the approach, we introduce an analogical similarity-based fitness measure that is computed through structure mapping. This setup enables the open-ended generation of networks analogous to a given base network. © 2014, Springer-Verlag Berlin Heidelberg.This work was supported by a JAE-Predoc fellowship from CSIC, and the research grants: 2009-SGR-1434 from the Generalitat de Catalunya, CSD2007-0022 from MICINN, and Next-CBR TIN2009-13692-C03-01 from MICINN.Peer Reviewe