What Is Computational Intelligence?
Computational intelligence recognizes and simulates intelligence from the perspective of biological evolution. According to this view, intelligence arises from the genetics, mutations, growth, and natural selection of the external environment. In the process of using retirement, survival of the fittest, the highly adaptable (mind) structure is preserved, and the level of intelligence also increases. So computing intelligence is intelligence based on structural evolution.
Computational intelligence
- The main methods of computational intelligence are
- Computational Intelligence
- Calculate the intelligent pinyin name Jìsuàn zhìnéng
- Book "Computing Intelligence" Information 1 [1]
- Make
- Folio: 16
- Price: 24.50 RMB
- brief introduction
- This book consists of 9 chapters. It introduces the main principles, techniques and methods of traditional intelligent technology, including knowledge representation, basic reasoning, uncertain reasoning, and search principles. It also introduces the main research and development directions of modern intelligent technology. , And discuss the practical problems of artificial intelligence and their solutions from the perspective of engineering applications.
- table of Contents
- Chapter 1 Introduction
- Chapter 2 Knowledge Representation
- Chapter 3 Basic Reasoning Principles
- Chapter 4 Search Principles
- Chapter 5 Fuzzy Logic
- Chapter 6 Neural Networks
- Chapter 7 Evolutionary Computing
- Chapter 8 Swarm Intelligence
- Chapter 9 Data Mining
- ...
- Book "Computing Intelligence" Information 2
- Author: Zhang Jun
- Publisher: Tsinghua University Press
- Publication time: November 2009
- ISBN: 9787302208440
- Folio: 16
- Price: 23.00 RMB
- brief introduction
- "Computational Intelligence" introduces the main algorithms in the field of computational intelligence, focusing on the source of ideas, process structure, development and improvement, parameter setting and related applications of various algorithms, including introduction and neural networks, fuzzy logic, genetic algorithms, ant colony Typical algorithms in the field of computational intelligence, such as optimization algorithms, particle swarm optimization algorithms, immune algorithms, distribution estimation algorithms, memetic algorithms, simulated annealing algorithms, and tabu search algorithms. "Computational Intelligence" is easy to understand, with pictures and texts, and easy to understand. There are not a lot of difficult formulas, theorems, and proofs in other algorithm books. Instead, a large number of chart examples are used to explain and introduce each algorithm. "Computational Intelligence" not only provides the flowchart and pseudo code of the algorithm implementation, but also explains the method and use of the algorithm through specific application examples. It also provides a lot of classic and important reference materials for readers to further study and Understanding the algorithm provides convenience. "Computational Intelligence" is suitable as an optional course textbook for undergraduates and graduate students in related majors, and is particularly suitable as an introductory textbook to meet the introductory needs of algorithm beginners to understand and learn computational intelligent algorithms. Reference books and reference books.
- Preface
- Since the advent of computers, artificial intelligence (AI) has been one of the goals pursued by computer scientists. As an important field of artificial intelligence, Computational Intelligence (CI) has attracted the attention of many researchers because of its intelligence, parallelism, and robustness. At present, many breakthroughs have been made in algorithm theory and algorithm performance, and they have been widely used in various fields, playing an important role in scientific research and production practice.
- Computational intelligence is a collective term for a class of algorithms designed inspired by the wisdom of nature and human intelligence. With the advancement of technology, the problems encountered in scientific research and engineering practice have become more and more complex. Using traditional computing methods to solve these problems faces problems such as high computational complexity and long computing time, especially for some Non-determlnlstlc Polynomal (NP) is a difficult problem, and traditional algorithms cannot find accurate solutions in a tolerable time. Therefore, in order to achieve a balance between solution time and accuracy, computer scientists have proposed many computationally intelligent algorithms with heuristic features. These algorithms either imitate the evolutionary process of the biological world, or imitate the physiological structure and physical functions of living things, or imitate the group behavior of animals, or imitate the characteristics of human thinking, language, and memory processes, or imitate physical phenomena in nature. The wisdom of nature and humans achieves optimal solutions to problems and solves acceptable solutions in an acceptable time. These algorithms together constitute the computational intelligent optimization algorithm.
- At present, computational intelligence algorithms have received widespread attention at home and abroad, and have become important research directions in artificial intelligence and computer science. Computational intelligence is still in the process of continuous development and improvement. At present, there is no solid mathematical foundation. Many researchers at home and abroad are also advancing in continuous exploration. Computational intelligence technology is constantly improved in the improvement of its own performance and the expansion of its application range. The research, development, and application of computational intelligence, whether it is the size of the research team, the number of published papers, or the information resources on the Internet, are developing rapidly, and have been widely recognized by the international academic community. , Image processing, automatic control, economic management, mechanical engineering, electrical engineering, communication networks, and biomedicine have achieved successful applications in various fields, including defense, science and technology, economics, industry and agriculture.
- Editor's Choice
- Computational Intelligence Features
- Computational Intelligence introduces the main algorithms in the field of computational intelligence. Its main features include:
- For beginners in algorithms, "Computing Intelligence" is easy to understand. "Computational Intelligence" focuses on the introduction of various algorithms' thought sources, process structure, development and improvement \ parameter settings and related applications, so that readers have a comprehensive understanding and understanding.
- For researchers of algorithms, Computational Intelligence is highly practical. "Computational Intelligence" not only tracks and reviews the development history and research status of various algorithms, but also provides a lot of classic and important reference materials, which provides readers with a deeper understanding and understanding of algorithms.
- "Computing Intelligence" is full of pictures and explanations. Computational Intelligence avoids a lot of difficult formulas, theorems, and proofs in other algorithm books. Instead, it explains and introduces each algorithm through a large number of chart examples. Algorithmic impression.
- "Computational Intelligence" provides specific implementation flowcharts and pseudo-codes for related computational intelligent algorithms, which is convenient for readers to understand and can be used as a reference tool for engineering and technical personnel to implement algorithms.
- "Computational Intelligence" introduces various algorithms to explain the specific method and use of the algorithm through some typical application examples to deepen readers' knowledge and understanding of the algorithm.
- table of Contents
- Chapter 1 Introduction
- Chapter 2 Neural Networks
- Chapter 3 Fuzzy Logic
- Chapter 4 Genetic Algorithms
- Chapter 5 Ant Colony Optimization Algorithm
- Chapter 6 Particle Swarm Optimization Algorithms
- Chapter 7 Immune Algorithms
- Chapter 8 Distribution Estimation Algorithms
- Chapter 9 The Memetic Algorithm
- Chapter 10 Simulated Annealing and Tabu Search
- Appendix A Index