Above are papers related to the following paper: [Paper_ID] Paper #594039. 	[Title] Hierarchical Learning with Procedural Abstraction Mechanisms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593104. 	[Title] Genetic Programming Bloat with Dynamic Fitness [Abstract] Technical Report: CSRP-97-29, 3 December 1997 Abstract In artificial evolution individuals which perform as their parents are usually rewarded identically to their parents. We note that Nature is more dynamic and there may be a penalty to pay for doing the same thing as your parents. We report two sets of experiments where static fitness functions are firstly augmented by a penalty for unchanged offspring and secondly the static fitness case is replaced by randomly generated dynamic test cases. We conclude genetic programming, when evolving artificial ant control programs, is surprisingly little effected by large penalties and program growth is observed in all our experiments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #593942. 	[Title] Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not [Abstract] The problem of programming an artificial ant to follow the Santa Fe trail is used as an example program search space. Analysis of shorter solutions shows they have many of the characteristics often ascribed to manually coded programs. Enumeration of a small fraction of the total search space and random sampling characterise it as rugged with many multiple plateaus split by deep valleys and many local and global optima. This suggests it is difficult for hill climbing algorithms. Analysis of the program search space in terms of fixed length schema suggests it is highly deceptive and that for the simplest solutions large building blocks must be assembled before they have above average fitness. In some cases we show solutions cannot be assembled using a fixed representation from small building blocks of above average fitness. These suggest the Ant problem is difficult for Genetic Algorithms. Random sampling of the program search space suggests on average the density of global optima changes only slowly with program size but the density of neutral networks linking points of the same fitness grows approximately linearly with program length. This is part of the cause of bloat. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129015. 	[Title] Boolean Functions Fitness Spaces [Abstract] We investigate the distribution of performance of the Boolean functions of 3 Boolean inputs (particularly that of the parity functions), the always-on-6 and even-6 parity functions. We us enumeration, uniform Monte-Carlo random sampling and sampling random full trees. As expected XOR dramatically changes the fitness distributions. In all cases once some minimum size threshold has been exceeded, the distribution of performance is approximately independent of program length. However the distribution of the performance of full trees is different from that of asymmetric trees and varies with tree depth. We consider but reject testing the No Free Lunch (NFL) theorems on these functions. Please summarise all the given information.

Paper #594039 has the title `Hierarchical Learning with Procedural Abstraction Mechanisms  
` and the abstract `nan`.
Question: Which category should Paper #594039 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #594039 has the title `Hierarchical Learning with Procedural Abstraction Mechanisms  
` and the abstract `nan`.
Question: Which category should Paper #594039 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