Abstract
This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global function optimization problem. In the ALRT, the emergent colony is a fundamental mechanism to search the optimum solution and can be accomplished through the metabolism, movement and reproduction among artificial organisms which appear at the optimum locations in the artificial world. In this case, the optimum locations mean the optimum solutions in the optimization problem. Hence, the ALRT focuses on the searching for the optimum solution in the location of emergent colonies and can achieve more accurate global optimum. The optimization results using different types of test functions are presented to demonstrate the described approach successfully achieves optimum performance. The algorithm is also applied to the test function optimization and optimum design of short journal bearing as a practical application. The optimized results are compared with those of genetic algorithm and successive quadratic programming to identify the optimizing ability.
TopBackground
There are two types of modeling approaches for studying natural phenomena; namely, the top-down approach involving a complicated, centralized controller that makes decisions based on access to all aspects of the global state; and the bottom-up approach, which is based on parallel, distributed networks of relatively simple, low-level agents that simultaneously interact with each other. Most traditional artificial intelligence (AI) research focuses on the former approach (Kim & Cho, 2006).
Artificial life (ALife), as a scientific term, was first stated in 1987 by Langton, who has contributed significantly to ALife. ALife is the study of man-made systems that exhibit behavior characteristics of natural living systems (Langton, 1989; Assad & Packard, 1992). The research motive of ALife was originated from the intent to understand the true meaning of life through the synthesis of life that makes it superior to the existing life in nature. ALife includes computational simulations such as virtual places where animated characters interact with the environment and with other virtual beings of the same or distinct categories.
A general property of ALife is that the whole system’s behavior is represented only directly, and arises out of interactions of individuals with each other. In this context, known as the philosophy of decentralized architecture, ALife shares important similarities with some new trends such as connectionism (Haykin, 1998), multi-agent AI (Ferber, 1999) and evolutionary computation (EC) (Goldberg, 1989). Technologies in ALife research include cellular automata, the Lindenmayer system (L-system), genetic algorithm (GAs), and neural networks (NNs).
Key Terms in this Chapter
Metabolism: The complete set of chemical reactions that occur in living cells. These processes are the basis of life, allowing cells to grow and reproduce, maintain their structures, and respond to their environments.
Food Chain: The flow of energy from one organism to the next. Organisms in a food chain are grouped into trophic levels based on how many links they are removed from the primary producers.
Reproduction: The biological process by which new individual organisms are produced. Reproduction is a fundamental feature of all known life; each individual organism exists as the result of reproduction.
Optimization: The study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set.
Artificial Organisms: Individuals of several species which live in an artificial world.
Artificial Life (ALife): The study of man-made systems that exhibit behavior characteristics of natural living systems. A field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations. ALife is the name given to a new discipline that studies “natural” life by attempting to recreate biological phenomena from scratch within computers and other “artificial” media. ALife complements the traditional analytic approach of traditional biology with a synthetic approach in which, rather than studying biological phenomena by taking apart living organisms to see how they work, one attempts to put together systems that behave like living organisms (Chris G. Langton).
Artificial World (AWorld): The space where the lowest and the highest limits are ximin, ximax ? Rn (i = 1, 2, ···, n), respectively. The artificial world is the world encompassing all things that are man-made. The ALife environment is a two or three-dimensional space where the artificial individuals can move around. During the single iteration (life cycle) all the living individuals move in the space interacting with other individuals exchanging information.