Above are papers related to the following paper: [Paper_ID] Paper #1109017. 	[Title] Basic PSugal an extension package for the development of Distributed Genetic Algorithms [Abstract] This paper presents the extension package developed by the author at the Faculty of Sciences and Technology of the New University of Lisbon, designed for experimentation with Coarse-Grained Distributed Genetic Algorithms (DGA). The package was implemented as an extension to the Basic Sugal system, developed by Andrew Hunter at the University of Sunderland, U.K., which is primarily intended to be used in the research of Sequential or Serial Genetic Algorithms (SGA). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116552. 	[Title] Empirical studies of the genetic algorithm with non-coding segments [Abstract] The genetic algorithm (GA) is a problem solving method that is modelled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of non-coding segments on GA performance. Non-coding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on non-coding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article, we discuss our hypotheses on non-coding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of non-coding segments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40. 	[Title] Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques [Abstract] This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We then introduce a technique for automatically designing and tuning the fuzzy knowledge-base system using GAs. Results from initial experiments show a performance improvement over a simple static GA. One Dynamic Parametric GA system designed by our automatic method demonstrated improvement on an application not included in the design phase, which may indicate the general applicability of the Dynamic Parametric GA to a wide range of ap plications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114442. 	[Title] Soft Computing: the Convergence of Emerging Reasoning Technologies [Abstract] The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #141342. 	[Title] Dynamic Parameter Encoding for Genetic Algorithms [Abstract] The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models. Please summarise all the given information.

Paper #1109017 has the title `Basic PSugal an extension package for the development of Distributed Genetic Algorithms  
` and the abstract `This paper presents the extension package developed by the author at the Faculty of Sciences and Technology of the New University of Lisbon, designed for experimentation with Coarse-Grained Distributed Genetic Algorithms (DGA). The package was implemented as an extension to the Basic Sugal system, developed by Andrew Hunter at the University of Sunderland, U.K., which is primarily intended to be used in the research of Sequential or Serial Genetic Algorithms (SGA). 
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Question: Which category should Paper #1109017 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1109017 has the title `Basic PSugal an extension package for the development of Distributed Genetic Algorithms  
` and the abstract `This paper presents the extension package developed by the author at the Faculty of Sciences and Technology of the New University of Lisbon, designed for experimentation with Coarse-Grained Distributed Genetic Algorithms (DGA). The package was implemented as an extension to the Basic Sugal system, developed by Andrew Hunter at the University of Sunderland, U.K., which is primarily intended to be used in the research of Sequential or Serial Genetic Algorithms (SGA). 
`.
Question: Which category should Paper #1109017 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Genetic_Algorithms