Evolutionary Machine Design: Methodology & ApplicationsNadia Nedjah, Luiza de Macedo Mourelle In recent years, genetic programming has attracted many researcher's attention and so became a consolidated methodology to automatically create new competitive computer programs. Concise and efficient synthesis of a variety of systems has been generated by evolutionary computations. Evolvable hardware is a growing discipline. It allows one to evolve creative and novel hardware architectures given the expected input/output behaviour. There are two kinds of evolvable hardware: extrinsic and intrinsic. The former relies on a simulated evolutionary process to evaluate the characteristics of the evolved designs while the latter uses hardware itself to do so. Usually, reconfigurable hardware such FPGA and FPAA are exploited. One of the main problems that still faces researchers in the field of evolutionary machine design is the scalability. This book is devoted to reporting innovative and significant progress in automatic machine design. Theoretical as well as practical chapters are contemplated. The scalability problem in evolutionary machine designs is addresses. The content of this book is divided into two main parts: evolvable hardware and genetic programming; and evolutionary designs. In the following, we give a brief description of the main contribution of each of the included chapters. |
Contents
| 3 | |
8 | 28 |
Bibliography | 57 |
Bibliography | 77 |
Decoding MEP Chromosomes and Fitness Assignment Process | 83 |
Decoding LGP Individuals | 90 |
Secure Evolvable Hardware for PublicKey | 111 |
Automated Discovery of Innovative Designs of Mechanical Components Using | 139 |
Comparison with NSGAII Alone | 152 |
Toward Efficient Topological Synthesis of Dynamic Systems Using Genetic Pro | 165 |
6 | 183 |
Bibliography | 194 |
140 | 205 |
141 | 217 |
Common terms and phrases
alignment allows analog Applications approach attached automated automatically average benchmark blocks bond graph called chapter chromosome complex components Conference connecting controlled create crossover cycle described domain dynamic edge efficient eigenvalue electrical elements encoding Engineering evaluations evolution evolutionary evolved circuit example experiments expression Figure fitness function Fuzzy gates gene genetic algorithm genetic programming given hardware hybrid increases individual initial input length local-search machine maximum mechanisms method modifiable modules multiple mutation needs node non-dominated front NSGA-II obtained operator optimal output parameters patented performance permutation plate population position possible problem procedure produced representation represents reversal robots selected sequence shapes shown shows signal signed single solutions solve sorting space specific Step structure success rate symbol synthesis Table terminal topology tree vertex cover vertices weight
Popular passages
Page 193 - G = (V, E). where V is the set of vertices and E is the set of edges.
Page 4 - The aim [is] ... to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence.
Page 6 - That is, the functions in the circuit-constructing program tree progressively side-effect the embryo and its successors until a fully developed circuit eventually emerges. The functions are applied in a breadth-first order. The functions in the circuit-constructing program trees are divided into five categories: (1...
Page 23 - The best-of-run evolved circuit (Figure 10-5) was produced in generation 182 and has an average error of 4.02 millivolts. The patented circuit had an average error of 6.76 millivolts. That is, the evolved circuit has approximately 59% of the error of the patented circuit over our four fitness cases. The claims in US patent 6,160,427 amount to a very specific description of the patented circuit.
Page 5 - ... defining a fitness measure that measures how well the behavior and characteristics of a candidate circuit satisfy high-level design requirements.
Page 152 - Department of Computer Science and Engineering, Michigan State University East Lansing, MI 48824, USA {sskang,mutka}(0cse.
Page 101 - There are several representations that have been used with success: binary encoding which is the most common mainly because it was used in the first works on genetic algorithms, represents an individual as a string of bits; permutation encoding mainly used in ordering problem, encodes an individual as a sequence of integer; value encoding represents an individual as a sequence of values that are some evaluation of some aspect of the problem [8], [9].
Page 18 - ... germane to electrical circuits. The principles of developmental biology provide the motivation for mapping trees into circuits by means of a developmental process that begins with a simple embryo. For circuits, the initial circuit typically includes a test fixture consisting of certain fixed components (such as a source resistor, a load resistor, an input port, and an output port) as well as an embryo consisting of one or more modifiable wires. Until the modifiable wires are modified, the circuit...
Page 11 - A result cannot acquire the rating of 'human competitive' merely because it is endorsed by researchers inside the specialized fields that are attempting to create machine intelligence. A result produced by an automated method must earn the rating of 'human competitive' independently of the fact that it was generated by an automated method.
Page 10 - The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal.