II. NEUROEVOLUTION
Neuroevolution is a form of machine learning that makes use of evolution as another form of adaptation in addition to learning. Evolutions of ANN occur via evolutionary algorithms (EA). These evolutionary algorithms has the roles of performing various task, such as rule extractions, connection weight training architecture designs and so on. All these process leads to the adaptability of the evolved ANN to change in their surrounding environment and also adapt to the environment itself. Several evolutionary algorithms have been developed over the years. The developments of these evolutionary algorithms are based on a specific framework as shown in figure 9. The various dialects of evolutionary algorithms differ only in technical details. One typical example of the difference among the EAs is the candidate solutions: strings of finite alphabet in Genetic algorithms (GA) [4], real value vectors in evolution strategies (ES) [5], and trees in Genetic programming (GP) [6]. The appeal toward evolution became apparent as it seems well suited for some of the most pressing computational problems in many fields. These problems include probing across a huge number of possibilities for solutions. One such example is the classification of large volumes of information and also in the processing of high dimensionality [7]. The above problem is hugely benefitted with the effective use of parallelism, whereby different pathways are explored simultaneously in an efficient way. Finally, real world problems are much too complex and are fractal in nature. Therefore, it is rather difficult to compute a program to tackle these real world problems. Computer programs that are handwritten are mostly limited to structural boundaries, t...
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A complex adaptive system is entity of networks and connections. It can “learn and adapt to change over time” which can change the “structure of the system” (Clancy, Effken, Pesut, 2008). It contains twelve elements: autopoesis or self-regenerization, open exchange, participation in networks, fractals, phase transition between order and chaos, search for fitness peaks, nonlinear dynamics, sensitive dependence, attractors that limit growth, strange attractors of emergence...
The advent of neural net with the seminal work of Hopfield , popularized the use of machine intelligence techniques in the pattern recognition. However, the dense and inherent structure of neural networks is not suitable for VLSI implementation. So, researchers in the neural network domain tried to simplify the structure of the neural network by pruning unnecessary connections. Simultaneously, the CA research community explored the advantages of the sparse network structure of cellular automata for relevant applications. The hybridization of cellularity and neural network has given rise to the popular concept of cellular neural networks.
Nairne, J. S., Smith, M. S., and Lindsay, D. S. (2001). Psychology: The Adaptive Mind. Scarborough: Nelson Thomson Learning.
Genetic Algorithms provide a holistic search process based on principles of natural genetics and survivals of the fittest……
Lycan, W. G. (1980) Reply to: "Minds, brains, and programs", The B.B.S. 3, p. 431.
Goertzel, B., & Pennachin, C. (2007). In Artificial General Intelligence. Heidelburg, New York: Springer Berlin. Retrieved on July 31, 2010 from Google books Database.
Below I will analyse the most important components of an evolutionary computation algorithm and explain how it works.
Artificial Intelligence (AI) is one of the newest fields in Science and Engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956 by John McCarthy. Artificial Intelligence is an art of creating machines that perform functions that require intelligence when performed by people [Kurzweil, 1990]. It encompasses a huge variety of subfields, ranging from general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on the crowded street, and diagnosing diseases. Artificial Intelligence is relevant to any intellectual task; it is truly a Universal field. In future, intelligent machines will replace or enhance human’s capabilities in
This approach includes two processes, training and classification (Chelali, Djeradi & Dejradi, 2009). In the training process, a subspace will be established by using the training samples, and then the training faces will be projected onto the same subspace. In the classification process, the input face image will be measured by Euclidean Distance to the subspace, and a decision will be made, either accept or reject.
Churchland, D. P. Theories of Brain Function. In : Neurophilosophy: Towards a Unified Science of Mind and Brain, MIT Press, 1986.
In the next part of the article Fisher’s model on geometric adaptation is presented. This model presents small step evolution or gradualism. It conveys the effect that spontaneous mutations have on various traits. Fisher argued through his model that more complex spe...
The most basic elements of a neural network, the artificial neurons, are modeled after the neurons of the brain. The "real" neuron is composed of four parts: the dendrites, soma, axon, and the synapse. The dendrites receive input from other neuron's synapses, the soma processes the information received, the axon carries the action potential which fires the neuron when a threshold is breached, and the synapse is where the neuron sends its output, which are in the form of neurotransmitters, to the dendrites of other neurons. Each neuron in the human brain can connect with up to 200,000 other neurons. The power and processing of the human brain comes from multitude of these basic components and the many thousands of connections between them.
Artificial Intelligence is the scientific theory to advance the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. This is going to hold the key in the future. It has always fa...
Summary- This book expert describes the fundamentals, history, and changes associated with Artificial Intelligence from 1950’s onward. The book provides a basic explanation that Artificial Intelligence involves simulating human behavior or performance using encoded thought processes and reasoning with electronic free standing components that do mechanical work.
Searle, John R. “Minds, Brains, and Programs.” The Philosophy of Artificial Intelligence. Margaret A. Boden, ed. New York: Oxford UP, 1990. 67-88.