Introduction to Computational Intelligence in Java

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Introduction to Computational Intelligence
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"Keep it simple: as simple as possible, but no simpler." - A. Einstein A major thrust in algorithmic development is the design of algorithmic models to solve increasingly complex problems. Enormous successes have been achieved through the modeling of biological and natural intelligence, resulting in so-called "intelligent systems". These intelligent algorithms include artificial neural networks, evolutionary computing, swarm intelligence, and fuzzy systems. Together with logic, deductive reasoning, expert systems, case-based reasoning and symbolic machine learning systems, these intelligent algorithms form part of the field of Artificial Intelligence (AI). Just looking at this wide variety of AI techniques, AI can be seen as a combination of several research disciplines, for example, computer science, physiology, philosophy, sociology and biology. But what is intelligence Attempts to find definitions of intelligence still provoke heavy debate. Dictionaries define intelligence as the ability to comprehend, to understand and profit from experience, to interpret intelligence, having the capacity for thought and reason (especially to a high degree). Other keywords that describe aspects of intelligence include creativity, skill, consciousness, emotion and intuition. Can computers be intelligent This is a question that to this day causes more debate than do definitions of intelligence. In the mid-1900s, Alan Turing gave much thought to this question. He believed that machines could be created that would mimic the processes of the human brain. Turing strongly believed that there was nothing the brain could do that a well-designed computer could not. Fifty years later
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CHAPTER 1. INTRODUCTION TO COMPUTATIONAL
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his statements are still visionary. While successes have been achieved in modeling biological neural systems, there are still no solutions to the complex problem of modeling intuition, consciousness and emotion - which form integral parts of human intelligence. In 1950 Turing published his test of computer intelligence, referred to as the Turing test [Turing 1950]. The test consisted of a person asking questions via a keyboard to both a person and a computer. If the interrogator could not tell the computer apart from the human, the computer could be perceived as being intelligent. Turing believed that it would be possible for a computer with 109 bits of storage space to pass a 5-minute version of the test with 70% probability by the year 2000. Has his belief come true The answer to this question is left to the reader, in fear of running head first into another debate! However, the contents of this book may help to shed some light on the answer to this question. A more recent definition of artificial intelligence came from the IEEE Neural Networks Council of 1996: the study of how to make computers do things at which people are doing better. A definition that is flawed, but this is left to the reader to explore in one of the assignments at the end of this chapter. This book concentrates on a sub-branch of AI, namely Computational Intelligence (CI) - the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. These mechanisms include those AI paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate. The following CI paradigms are covered: artificial neural networks, evolutionary computing, swarm intelligence and fuzzy systems. While individual techniques from these CI paradigms have been applied successfully to solve real-world problems, the current trend is to develop hybrids of paradigms, since no one paradigm is superior to the others in all situations. In doing so, we capitalize on the respective strengths of the components of the hybrid CI system, and eliminate weaknesses of individual components. The rest of this book is organized as follows: Section 1.1 presents a short overview of the different CI paradigms, also discussing the biological motivation for each paradigm. A short history of AI is presented in Section 1.2. Artificial neural networks are covered in Part II, evolutionary computing in Part III, swarm intelligence in Part IV and fuzzy systems in Part V. A short discussion on hybrid CI models is given in the conclusion of this book. At this point it is necessary to state that there are different definitions of what constitutes CI. This book reflects the opinion of the author, and may well cause some debate. For example, Swarm Intelligence (SI) is classified as a CI paradigm, while most researchers are of the belief that it belongs only under Artificial Life. However, both Particle Swarm Optimization (PSO) and Anto Colony Optimization (AGO), as treated under SI, satisfy the definition of CI given above, and are therefore
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