ABSTRACT
Genetic algorithm sounds like terminology from a B-rated sci-fi movie. Just what is a genetic algorithm? Is it human? Is it a computer? Is it alive? Is it the mutant offspring from some defunct Government experiment? All of these questions, and more, will be answered within the pages of this paper. The adventure will begin with a trip back in time to the roots of genetic algorithms. From there, the journey will press on to the inventor, or the father of genetic algorithms, Dr. John H. Holland at the University of Michigan. Finally, moving forward in time, covering a span of over twenty years from the inception of the genetic algorithm to its present day representations and applications, the terminology and concepts behind these algorithms will be explored.
INTRODUCTION
"God . . . created a number of possibilities in case some of
His prototypes failed-that is the meaning of evolution."
Graham Greene (1904-91), British novelist.
Mr. Visconti, in Travels With My Aunt, pt. 2, ch. 7 (1969).
Nature - it is all around us. We see it everyday, and we are even a part of it. It is so simple, but yet, so complex and over thousands of years, it has changed to adapt to its environment. This process of change is called evolution. It is not a process that we are overtly aware of, however, we are products of it. It can be said that the process of evolution is a process of adaptation. Adaptation is the part of evolution that has captivated computer scientists since the beginning of the computer age. Back in the 1960's the process of adaptation intrigued John Holland. This intrigue led him to study it formally. He believed that somehow this process, or the mechanisms of this process, could be captured in a computer. He pr...
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...interesting to see just where genetic algorithms turn up next, and how they have been applied to that application.
BIBLIOGRAPHY
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Zhao, Buyun. "Charles Darwin & Evolution." Charles Darwin & Evolution. Christ's College, 2009. Web. 04 May 2014.
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Anyone with even a moderate background in science has heard of Charles Darwin and his theory of evolution. Since the publishing of his book On the Origin of Species by Means of Natural Selection in 1859, Darwin’s ideas have been debated by everyone from scientists to theologians to ordinary lay-people. Today, though there is still severe opposition, evolution is regarded as fact by most of the scientific community and Darwin’s book remains one of the most influential ever written.
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"Eugenics, Genetic Engineering Lite." The Future of Human Evolution. Humans Future, 2010. Web. 14 Feb 2012.
Genetic Algorithms provide a holistic search process based on principles of natural genetics and survivals of the fittest……
According to Darwin and his theory on evolution, organisms are presented with nature’s challenge of environmental change. Those that possess the characteristics of adapting to such challenges are successful in leaving their genes behind and ensuring that their lineage will continue. It is natural selection, where nature can perform tiny to mass sporadic experiments on its organisms, and the results can be interesting from extinction to significant changes within a species.
Man is the product of heredity and environment and that he acts as his machine responds to outside stimuli and nothing else, seem amply proven by the evolution and history of man. Every process of nature and life is a continuous sequence of cause and effect (156).
Second Paper “I shall briefly explain how I conceive of this matter. Look round the world: Contemplate the whole and every part of it: You will find it to be nothing but one great machine, subdivided into an infinite number of lesser machines, which again admit subdivisions, to a degree beyond what human senses and faculties can trace and explain. All these various machines, and even their most minute parts, are adjusted to each other with an accuracy, which ravishes into admiration all men, who have ever contemplated them. The curious adapting of means to ends, throughout all nature, resembles, though it much exceeds, the production of human contrivance; of human design, thought, wisdom, and intelligence. Since therefore the effects resemble each other, we are led to infer, by all the rules of analogy, that the causes also resemble; and that the author of nature is somewhat similar to the mind of man; though possessed of much larger faculties, proportioned to the grandeur of the work, which he has executed.
Mifflin, Houghton. "artificial selection science definition." Your Dictionary. Houghton Mifflin Harcourt, 2010. Web. 10 May 2014.
All living species will never stop evolving over time due to one important key concept incorporated in the theory of evolution: natural selection (lecture). Impressive, isn’t it? To think that this mechanism has always been present to help the survival of the breathing kind (lecture). Therefore, the objective of this essay consists of demonstrating the notions about evolution and its four mechanisms, the process of natural selection, the theory of natural selection and types of evidence for evolution.
Cormen T. H, Leiserson C. E., Rivest R. L. and Stein C. [1990] (2001). “Introduction to Algorithms”, 2nd edition, MIT Press and McGraw-Hill, ISBN 0-262-03293-7, pp. 27–37. Section 2.3: Designing algorithms..
Optimization, in simple terms, means minimize the cost incurred and maximize the profit such as resource utilization. EAs are population based metaheuristic (means optimize problem by iteratively trying to improve the solution with regards to the given measure of quality) optimization algorithms that often perform well on approximating solutions to all types of problem because they do not make any assumptions about the underlying evaluation of the fitness function. There are many EAs available viz. Genetic Algorithm (GA) [1] , Artificial Immune Algorithm (AIA) [2], Ant Colony Optimization (ACO) [3], Particle Swarm Optimization (PSO) [4], Differential Evolution (DE) [5, 6], Harmony Search (HS) [7], Bacteria Foraging Optimization (BFO) [8], Shuffled Frog Leaping (SFL) [9], Artificial Bee Colony (ABC) [10, 11], Biogeography-Based Optimization (BBO) [12], Gravitational Search Algorithm (GSA) [13], Grenade Explosion Method (GEM) [14] etc. To use any EA, a model of decision problem need to be built that specifies: 1) The decisions to be made, called decision variables, 2) The measure to be optimized, called the objective, and 3) Any logical restrictions on potential solutions, called constraints. These 3 parameters are necessary while building any optimization model. The solver will find values for the decision variables that satisfy the constraints while optimizing (maximizing or minimizing) the objective. But the problem with all the above EAs is that, to get optimal solution, besides the necessary parameters (explained above), many algorithms-specific parameters need to be handled appropriately. For example, in case of GA, adjustment of the algorithm-specific parameters such as crossover rate (or probability, PC), mu...
Lamarck proposed that organisms: Have an innate tendency toward complexity and perfection, have an innate tendency to become simpler as time passes, inherit all of the adaptations they display,