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عضو جدید
Speaker: Dr. Andrew L. Nelson was born Laramie Wyoming in 1967. He received his B.S. degree with concentration in Computer Science from the Evergreen State College in Olympia Washington in 1990. He received his M.S. in Electrical Engineering from North Carolina State University in 2000. He received his Ph.D. in Electrical Engineering at the Center for Robotics and Intelligent Machines (CRIM) at North Carolina State University in 2003. Between 2003 and 2005 he was a visiting researcher at the University of South Florida. Currently he is a researcher at Androtics LLC, Tucson AZ and Santa Cruz CA. His main interests are in the fields of fully autonomous robot control, bio-inspired robot control and evolutionary robotics. His robotics work has included applying artificial evolution to synthesize controllers for swarms of autonomous robots as well as the development of a fuzzy-logic based controllers for robot navigation. He pursues work in artificial neural networks, genetic algorithms and soft computing related to autonomous machine control. He has also conducted research in diverse fields including electric machine design and molecular biology.
Abstract: In recent years researchers interested in creating artificial life forms have turned their attention toward techniques involving artificial evolution. Evolutionary computing (EC) is a large, and rapidly growing area of research focused on exploiting computational processes that mimic natural evolution. Evolutionary computing techniques are applied to a wide range of optimization, classification and control problems. Although the particulars of implementation vary greatly, most evolutionary computing applications employ some variation on the following steps: A population of potential solutions to a particular problem (often referred to as candidate solutions) is generated. The individuals in this population are tested to determine how well each of them solves the problem, and the better performing solutions are selected. These better performing solutions are then altered by some stochastic process and then returned to the larger population of solutions to replace the most poorly performing individuals. The sequence of testing, selection, alteration and replacement is then repeated until a suitably proficient solution arises. A key component of this process - one might argue, the key component - is the measurement of fitness of the evolving candidate solutions. For many evolutionary computing applications, there are well-defined and efficient methods for determining the fitness of a given solution. However, determining fitness by using a function, or any type of measurement is unnatural.
Nature applies no particular criteria for survival. At a high level, the phrase "survival of the fittest" seems to confer some meaning to the concept of selective pressure in natural evolution, but in fact, this phrase reduces to little more than a truism: "survival of those that survive". The universe takes no action at any level beyond simple iteration of fundamental physical law. The persistence of structures or patterns of matter, including rocks, stellar material, and life-forms boils down to simple possibility: A pattern that is possible, given physical law and past configurations of matter, will exist, regulated only by stochastic factors embodied in the fundamental fabric of the universe. Things do not exist because they are better at existing. They exist because they are possible.
Fitness functions or objective functions work well for optimization and generation of particular well-defined solutions to many traditional problems, as has been demonstrated by the success of evolutionary computing. But what is the effect of attempting to use explicit fitness functions when trying to evolve life-like entities?
Nature applies no particular criteria for survival. At a high level, the phrase "survival of the fittest" seems to confer some meaning to the concept of selective pressure in natural evolution, but in fact, this phrase reduces to little more than a truism: "survival of those that survive". The universe takes no action at any level beyond simple iteration of fundamental physical law. The persistence of structures or patterns of matter, including rocks, stellar material, and life-forms boils down to simple possibility: A pattern that is possible, given physical law and past configurations of matter, will exist, regulated only by stochastic factors embodied in the fundamental fabric of the universe. Things do not exist because they are better at existing. They exist because they are possible.
Fitness functions or objective functions work well for optimization and generation of particular well-defined solutions to many traditional problems, as has been demonstrated by the success of evolutionary computing. But what is the effect of attempting to use explicit fitness functions when trying to evolve life-like entities?