Flight Control System Design Optimisation via Genetic Programming

Bourmistrova, Anna; Khantsis, Sergey

doi:10.5772/6470

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…The range of disturbances and the initial ship phase are chosen to provide a moderately conservative estimation of controller performance. Results for different dynamic scenarios are presented in (Bourmistrova & Khantsis, 2009). The parameters corresponding to aircraft geometry and configuration are tested in a similar manner.…”

Section: Controller Testingmentioning

confidence: 99%

New Achievements in Evolutionary Computation

Korošec¹

2010

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Evolutionary computation has been widely used in computer science for decades. Even though it started as far back as the 1960s with simulated evolution, the subject is still evolving. During this time, new metaheuristic optimization approaches, like evolutionary algorithms, genetic algorithms, swarm intelligence, etc., were being developed and new fields of usage in artificial intelligence, machine learning, combinatorial and numerical optimization, etc., were being explored. However, even with so much work done, novel research into new techniques and new areas of usage is far from over. This book presents some new theoretical as well as practical aspects of evolutionary computation.The first part of the book is mainly concentrated on evolutionary algorithms and their applications. First, the influence that diversity has on evolutionary algorithms will be described. There is also an insight into how to efficiently solve the constraint-satisfaction problem and how time series can be determined by the use of evolutionary forecasting. Quantum finite-state machines are becoming increasingly more important. Here, an evolutionary-based logic is used for its synthesis. With an ever increasing number of criteria being used to evaluate a solution, this is leading to different multi-objective evolutionary approaches. Such approaches are being applied to control optimization and phylogenetic reconstruction. It is well known that evolutionary-computation approaches are mostly bioinspired. So it is interesting to see how they can return to its origin by solving bio-problems. Here, they are used for predicting membrane protein-protein interactions and are applied to different bioinformatics applications.The second part of the book presents some other well-known evolutionary approaches, like genetic algorithms, genetic programming, estimations of the distribution algorithm, and swarm intelligence. Genetic algorithms are used in Q-learning to develop a compact control table, while flight-control system design is being optimized by genetic programming. A new estimation of the distribution algorithm, using the empirical selection distribution, is being presented and, on the other hand, a classical version is being applied to the video-tracking system problem. The book ends with the recently very popular swarm-intelligence approaches, where they are used in artificial societies, social simulations, and applied to the Chinese traveling-salesman problem. This book will be of great value to undergraduates, graduate students, researchers in computer science, and anyone else with an interest in learning about the latest developments in evolutionary computation.

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Section: Controller Testingmentioning

confidence: 99%

New Achievements in Evolutionary Computation

Korošec¹

2010

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“…where (Bourmistrova & Khantsis, 2009). The parameters corresponding to aircraft geometry and configuration are tested in a similar manner.…”

Section: Controller Testingmentioning

confidence: 99%

“…Application of the ED algorithm to control laws evolution is fairly straightforward: 1) preparation of the sample task for the controller, 2) execution of the simulation model for the given sample task and 3) analysis of the obtained performance and evaluation of the fitness value. For a greater detail of control system design reader is referred to (Bourmistrova & Khantsis, 2009). When both the model and fitness evaluation are prepared, the final evolution may be started.…”

Section: Controller Synthesis and Testingmentioning

confidence: 99%