Above are papers related to the following paper: [Paper_ID] Paper #1153900. 	[Title] ABSTRACT In general, the machine learning process can be accelerated through the use of additional [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128291. 	[Title] A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation [Abstract] We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search process. We investigate a collective memory search which utilizes a random search engine and find that it significantly outperforms the GP based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform better than a collective memory search employing random search as an engine. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153899. 	[Title] ABSTRACT [Abstract] In general, the machine learning process can be accelerated through the use of heuristic knowledge about the problem solution. For example, monomorphic typed Genetic Programming (GP) uses type information to reduce the search space and improve performance. Unfortunately, monomorphic typed GP also loses the generality of untyped GP: the generated programs are only suitable for inputs with the specified type. Polymorphic typed GP improves over mono-morphic and untyped GP by allowing the type information to be expressed in a more generic manner, and yet still imposes constraints on the search space. This paper describes a polymorphic GP system which can generate polymorphic programs: programs which take inputs of more than one type and produces outputs of more than one type. We also demonstrate its operation through the generation of the map polymorphic program. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #108047. 	[Title] Evolving a Team [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #594511. 	[Title] Performance Enhanced Genetic Programming [Abstract] Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process can be achieved with much greater efficiency through the use of a formally-based representation and strong typing. We report initial experimental results which demonstrate that our technique exhibits significantly better performance than previous work. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #54129. 	[Title] Strongly Typed Genetic Programming [Abstract] BBN Technical Report #7866: Abstract Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, arguments for functions, and values returned from functions must be of the same data type. To correct this deficiency, we introduce a variation of genetic programming called "strongly typed" genetic programming (STGP). In STGP, variables, constants, arguments, and returned values can be of any data type with the provision that the data type for each such value be specified beforehand. This allows the initialization process and the genetic operators to only generate syntactically correct parse trees. Key concepts for STGP are generic functions, which are not true strongly typed functions but rather templates for classes of such functions, and generic data types, which are analogous. To illustrate STGP, we present four examples involving vector/matrix manipulation and list manipulation: (1) the multi-dimensional least-squares regression problem, (2) the multi-dimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578898. 	[Title] Clique Detection via Genetic Programming  Topics in Combinatorial Optimization [Abstract] Genetic Programming is utilized as a technique for detecting cliques in a network. Candidate cliques are represented in lists, and the lists are manipulated such that larger cliques are formed from the candidates. The clique detection problem has some interesting implications to the Strongly Typed Genetic Programming paradigm, namely in forming a class hierarchy. The problem is also useful in that it is easy to add noise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578645. 	[Title] Entailment for Specification Refinement [Abstract] Specification refinement is part of formal program derivation, a method by which software is directly constructed from a provably correct specification. Because program derivation is an intensive manual exercise used for critical software systems, an automated approach would allow it to be viable for many other types of software systems. The goal of this research is to determine if genetic programming (GP) can be used to automate the specification refinement process. The initial steps toward this goal are to show that a well-known proof logic for program derivation can be encoded such that a GP-based system can infer sentences in the logic for proof of a particular sentence. The results are promising and indicate that GP can be useful in aiding pro gram derivation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578646. 	[Title] Type Inheritance in Strongly Typed Genetic Programming [Abstract] This paper appears as chapter 18 of Kenneth E. Kinnear, Jr. and Peter J. Angeline, editors Advances in Genetic Programming 2, MIT Press, 1996. Abstract Genetic Programming (GP) is an automatic method for generating computer programs, which are stored as data structures and manipulated to evolve better programs. An extension restricting the search space is Strongly Typed Genetic Programming (STGP), which has, as a basic premise, the removal of closure by typing both the arguments and return values of functions, and by also typing the terminal set. A restriction of STGP is that there are only two levels of typing. We extend STGP by allowing a type hierarchy, which allows more than two levels of typing. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #578649. 	[Title] Augmenting Collective Adaptation with Simple Process Agents [Abstract] We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. However, there is still considerable scope for improvement. In collective adaptation, search agents gather knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting the exploration of the search agents. We examine the utility of increasing the capabilities of the centralized process agents. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128319. 	[Title] Modeling Distributed Search via Social Insects [Abstract] Complex group behavior arises in social insects colonies as the integration of the actions of simple and redundant individual insects [Adler and Gordon, 1992, Oster and Wilson, 1978]. Furthermore, the colony can act as an information center to expedite foraging [Brown, 1989]. We apply these lessons from natural systems to model collective action and memory in a computational agent society. Collective action can expedite search in combinatorial optimization problems [Dorigo et al., 1996]. Collective memory can improve learning in multi-agent systems [Garland and Alterman, 1996]. Our collective adaptation integrates the simplicity of collective action with the pattern detection of collective memory to significantly improve both the gathering and processing of knowledge. As a test of the role of the society as an information center, we examine the ability of the society to distribute task allocation without any omnipotent centralized control. Please summarise all the given information.

Paper #1153900 has the title `ABSTRACT In general, the machine learning process can be accelerated through the use of additional
` and the abstract `nan`.
Question: Which category should Paper #1153900 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1153900 has the title `ABSTRACT In general, the machine learning process can be accelerated through the use of additional
` and the abstract `nan`.
Question: Which category should Paper #1153900 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