Based on a functional block diagram and signal flow of equipment, the fault information matrix of the equipment is structured, then a genetic programming algorithm (GPA) for finding the best strategy for fault isolati...
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Based on a functional block diagram and signal flow of equipment, the fault information matrix of the equipment is structured, then a genetic programming algorithm (GPA) for finding the best strategy for fault isolation, composed of some ordinal test points, is consequently developed, which can isolate the failed component with the fewest test points and the least time.
genetic programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generat...
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In this paper, a design methodology is proposed for generating a fuzzy rule-based classifier for imbalanced datasets. The classifier is based on Sugeno-type Fuzzy Inference System. It is generated by using of subtract...
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In this paper, a design methodology is proposed for generating a fuzzy rule-based classifier for imbalanced datasets. The classifier is based on Sugeno-type Fuzzy Inference System. It is generated by using of subtractive clustering and Multi-Gene genetic programming to obtain fuzzy rules. The subtractive clustering is utilized for producing the antecedents of rules and Multi-Gene genetic programming is employed for generating the functions in the consequence parts of rules. Feature selection is utilized as an important pre-processing step for dimension reduction. Experiments are performed with 8 datasets from KEEL. The comparison results reveal that the proposed classifier outperforms the other methods.
Systems Biology is an interdisciplinary field that aims to understand the interactions among biological components. A central focus of this field is modeling gene regulatory networks (GRN) and understanding how gene e...
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ISBN:
(数字)9798350308365
ISBN:
(纸本)9798350308372
Systems Biology is an interdisciplinary field that aims to understand the interactions among biological components. A central focus of this field is modeling gene regulatory networks (GRN) and understanding how gene expression varies. ScRNA -Seq technology has enabled the ability to explore gene expression at the single-cell level, unlike previous technologies where only an average view of gene expression was possible. As a result, the literature has observed a significant increase in the number of inference methods, taking into account the specificities of the data from scRNA -Seq profiling, such as batch effects, biological variations, and dropouts. However, recent studies have shown that the performance of GRN inference algorithms when considering scRNA -Seq technology is close to random predictors. Furthermore, algorithms that perform well on synthetic and curated data are different from those that perform well on experimental data, indicating a lack of robustness. Considering that experimental data is more interesting for biology, as the modeling of its GRNs enables the understanding of biological phenomena, in this paper we show that the CGPGRN framework can deal with experimental data. Computational experiments are carried out and the results indicate that CGPGRN can outperform state-of-the-art algorithms in several situations and is the only one capable of obtaining correct regulatory relationships in all situations considered.
In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these object...
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ISBN:
(纸本)9781509006243
In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these objectives separately or aggregated them into a single objective (fitness function) and treat the problem as a single-objective optimization. Very few studies attempted to solve the multi-objective JSS with two or three objectives, not to mention the many-objective JSS with more than three objectives. In this paper, we investigate the many-objective JSS, which takes all the objectives into account. On the other hand, dispatching rules have been widely used in JSS due to its flexibility, scalability and quick response in dynamic environment. In this paper, we focus on evolving a set of trade-off dispatching rules for many-objective JSS, which can generate non-dominated schedules given any unseen instance. To this end, a new hybridized algorithm that combines genetic programming (GP) and NSGA-III is proposed. The experimental results demonstrates the efficacy of the newly proposed algorithm on the tested job-shop benchmark instances.
This paper presents an experimental research on the size of individuals when fixed and dynamic size populationsare employed with genetic programming (GP). We propose an improvement to the Plague operator (PO), that we...
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ISBN:
(纸本)9781595936974
This paper presents an experimental research on the size of individuals when fixed and dynamic size populationsare employed with genetic programming (GP). We propose an improvement to the Plague operator (PO), that we have called Random Plague (RPO). Then by further studies based on the RPO results we analyzed the Fault Tolerance onParallel genetic programming.
作者：
H. de GarisBrain Builder Group
Evolutionary Systems Department ATR Human Information Processing Research Laboratories Kyoto Japan
The paper reports on a project which aims to build (i.e. grow/evolve) an artificial brain by the year 2001. This artificial brain should initially contain thousands of interconnected artificial neural network modules,...
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The paper reports on a project which aims to build (i.e. grow/evolve) an artificial brain by the year 2001. This artificial brain should initially contain thousands of interconnected artificial neural network modules, and be capable of controlling approximately 1000 "behaviors" in a "robot kitten". The name given to this research project is "CAM-Brain", because the neural networks (based on cellular automata) will be grown inside special hardware called cellular automata machines (CAMs). Using a family of CAMs, each with its own processor to measure the performance quality or fitness of the evolved neural circuits, will allow the neural modules and their interconnections to be grown/evolved at electronic speeds. State of the art in CAM design is about 10 to the power 9 or 10 cells. Since a neural module of about 15 connected neurons can fit inside a cube of 100 cells on a side (1 million cells), a CAM which is specially adapted for CAM-Brain could contain thousands of interconnected modules, i.e. an artificial brain.< >
Designing the behavioral attributes of a robot is challenging, and the complexity of this task is even more increased in the case of swarm robotics. For effectively solving such problems special types of evolutionary ...
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ISBN:
(数字)9781728183404
ISBN:
(纸本)9781728183411
Designing the behavioral attributes of a robot is challenging, and the complexity of this task is even more increased in the case of swarm robotics. For effectively solving such problems special types of evolutionary algorithms can be used such as genetic programming and Queen Bee Based genetic programming method. The revolutionary idea behind these algorithms is that they use tree based representation for the individuals in a population, thus being able to solve structure optimization problems. The goal of this paper is to introduce the idea of the Queen Bee Based genetic programming method and compare its effectiveness with genetic programming through the evolution of a successful hive based behavioral program.
This paper presents a comparative study between two data mining techniques: genetic programming (GP) and Deep Learning (DL). This comparison will be based on the cart pole balancing problem. We also compared the resul...
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This paper presents a comparative study between two data mining techniques: genetic programming (GP) and Deep Learning (DL). This comparison will be based on the cart pole balancing problem. We also compared the results with Q-Learning (QL), a classic algorithm that is also used in hybridizations with GP an DL for reinforcement learning problems. Our results presented that GP can rival DL for this kind of problem.
Tree encodings of programs are well known for their representative power and are used very often in genetic programming. In this paper we experiment with a new data structure, named straight line program (slp), to rep...
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Tree encodings of programs are well known for their representative power and are used very often in genetic programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described and new recombination operators for GP related to slp's are introduced. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.
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