The decomposition-based many-objective evolutionary algorithms (MaOEAs) generally decompose a many-objective optimization problem into several scalar subproblems for collaborative optimization using the aggregation fu...
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Proteins belong amongst one of the essential biological structures which influence processes occurring in living organisms. The elementary characterization of proteins has a form of sequences containing amino-acids wh...
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ISBN:
(纸本)9781618040275
Proteins belong amongst one of the essential biological structures which influence processes occurring in living organisms. The elementary characterization of proteins has a form of sequences containing amino-acids which are elements of primary protein structures. This paper is concerned with the description of primary protein structures by the evolutionary-programmed Turing machine. The description has an appearance of rules which are used for the representation of the Turing machine's program. It therefore means that it is possible to use a Turing machine for the description of original proteins on the basis of amino-acid sequences or the eventual reconstruction of the original protein if damage occurred.
An important factor in the successful application of evolutionary techniques to real-world problems is the incorporation of domain knowledge. One form such knowledge often takes is the possession of one or more high-q...
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To solve the problem of unsupervised anomaly detection, an unsupervised anomaly-detecting algorithm based on an evolutionary artificial immune network is proposed in this paper. An evolutionary artificial immune netwo...
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A novel quantum swarm evolutionary algorithm is presented based on quantum-inspired evolutionary algorithm in this article. The proposed algorithm adopts quantum angle to express Q-bit and improved particle swarm opti...
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One of the critical challenges in the implementation of machine learning algorithm is hyperparameter optimization as performance of any machine learning model is sensitive to the setting of their hyperparametersr. Evo...
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Many-objective optimization is of top challenge in multi-objective optimization research community. Various many-objective optimization problems exhibit difference characteristics and require the algorithm to be effec...
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With the rapid development of modern information technology, the corresponding computer music is also produced. How to apply artificial intelligence technology in the field of music has become a new research field, wh...
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Efficient history matching of highly uncertain reservoir models is important in many applications of the reservoir engineering area, such as reservoir management, production prediction, and development optimisation. H...
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ISBN:
(纸本)9781629937915
Efficient history matching of highly uncertain reservoir models is important in many applications of the reservoir engineering area, such as reservoir management, production prediction, and development optimisation. History matching has commonly been done manually by a tedious trial and error approach in which global and local adjustments are done to the model properties until a best model is obtained which honours production response of the reservoir model. With recent advances in computer science and hardware, assisted history matching techniques can improve the quality of the match within less time and effort. Stochastic evolutionary algorithms are popular methods in history matching and have been widely used to explore and search the global parameter search space and find multiple good fitting models. They are quite easy to implement, and if well applied, able to be globally convergent even in complex problems such as history matching. General critiques of these algorithms include high computational demands and demand for the tuning of the algorithm's control parameters. Adaptation methods for EAs adjust algorithm's control parameters during the evolution to improve the search quality and accelerate the convergence by guiding search toward appropriate regions of the search space. This insures a balance between exploration and exploitation properties of the algorithm. In this paper, we introduce a novel adaptive scheme for EAs, which intelligently adapts the control parameter that affects the diversity of the population. The adapted control parameter is number of selected to generated solutions in each generation, which controls the balance between exploration and exploitation. We apply proposed algorithm to optimisation problem of two test functions and history matching problem of a well-known synthetic reservoir simulation model. Our results show that, compared with the original EAs, adaptive EA is able to find better spread of fitting models with better conver
Multiagent evolutionary algorithm (MEA) is a relatively new optimization technique, where a life cycle of a population of agents, which perform local search, is simulated. The algorithm was originally intended as a me...
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ISBN:
(纸本)9781450319645
Multiagent evolutionary algorithm (MEA) is a relatively new optimization technique, where a life cycle of a population of agents, which perform local search, is simulated. The algorithm was originally intended as a method for solving the graph coloring problem and incorporates ideas such as lifespans of agents and a positive or negative reinforcement for the ability of the agent to improve fitness or its stagnation. In this paper, we propose to use MEA for optimization on NK fitness landscapes. These landscapes are popular for the tunability of their ruggedness and are a particularly interesting use case for MEA. This algorithm is especially well suited for functions, where local search tends to fail because of their multimodality. However, using many short-term local search subroutines in a well-tuned version of MEA can significantly improve the results of the same local search algorithm. Experimental results are presented for MEA with the simple (1+1) evolutionary Algorithm ((1+1) EA) used as a local search subroutine. These results show that in large and more rugged NK landscapes, MEA outperforms the multi-start (1+1) EA with number of parallel starts equal to the initial population size of MEA. This is the first time we obtained results, which clearly indicate that solely the emergent multiagent nature of MEA, driven by the lifespans and the reinforcement mechanism, is able to improve the results of multi-start local search.
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