Finally, the last part deals with the up-to-date field of parallel particle swarm optimization to illustrate the intrinsic similarities and potential extensions to techniques in this domain. The third part treats the problem of distributed evolutionary computation and presents three interesting applications wherein parallel EC new ideas are featured. The second part is about hardware implementations of genetic algorithms, a valuable topic which is hard to find in the present literature. algorithmic vision on parallel evolutionary optimizations. The first part deals with a clear software-like and. "Parallel Evolutionary Computation" focuses on the aspects related to the parallelization of evolutionary computations, such as parallel genetic operators, parallel fitness evaluation, distributed genetic algorithms, and parallel hardware implementations, as well as on their impact on several applications. The effectiveness of the approach is demonstrated through implementation in two applications from the domain of architecture. The result of this approach is that designers and decision makers have great certainty about the satisfaction of their goals and are able to concentrate on second order aspects they could not consider with great awareness prior to the computation. This is accomplished by using fuzzy information processing to deal with the vagueness of objectives, and multi-objective evolutionary algorithm to deal with the conflicts among the objectives. That is, the objects know ‘themselves’ what to do to satisfy the designer’s goals. Such objects exhibit intelligent behaviour in the sense that they approach the most desirable solutions for conflicting, vague goals put forward. This concept is termed intelligent design objects. The approach is based on a novel concept of the objects forming a design. The implication is that it is now possible to create games that generate their own content to satisfy players, potentially significantly reducing the cost of content creation and increasing the replay value of games.Īn approach to handle complexity issues in design is presented, where computation is used to reach the most suitable solutions. As shown in this paper, players can discover a wide variety of content that is not only novel, but also based on and extended from previous content that they preferred in the past. In GAR, players pilot space ships and fight enemies to acquire unique particle system weapons that are evolved by the game. To demonstrate this approach, the galactic arms race (GAR) video game is also introduced. To realize this ambition, this paper introduces the content-generating neuroevolution of augmenting topologies (cgNEAT) algorithm, which automatically evolves game content based on player preferences, as the game is played. However, ideally, if game content could be constantly renewed, players would remain engaged longer in the evolving stream of novel content. In most modern video games, the set of content shipped with the game is static and unchanging, or at best, randomized within a narrow set of parameters. Video game content includes the levels, models, items, weapons, and other objects encountered and wielded by players during the game. In addition to describing technical details, this paper concludes with a discussion of the scientific implications of the system. Creatures, available on Windows95 platforms since late 1996, offers users an opportunity to engage with Artificial Life technologies. Learning includes the ability to acquire a simple verb–object language.Additionally, both the network architecture and details of the biochemistry for a creature are specified by a variable-length genetic encoding, allowing for evolutionary adaptation through sexual reproduction. A biologically inspired learning mechanism allows the neural network to adapt during the lifetime of a creature. Each creature has a neural network responsible for sensory-motor coordination and behavior selection, and an artificial biochemistry that models a simple energy metabolism along with a hormonal system that interacts with the neural network to model diffuse modulation of neuronal activity and staged ontogenetic development. The internal architecture of the creatures is strongly inspired by animal biology. The agents (known as creatures) are intended as sophisticated virtual pets. Creatures provides a simulated environment in which exist a number of synthetic agents that a user can interact with in real-time. We present a technical description of Creatures, a commercial home-entertainment software package.
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