Above are papers related to the following paper: [Paper_ID] Paper #1129798. 	[Title] On the relationship between distributed group-behaviour and the behavioural complexity of individuals [Wilson Sober 1994]) [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1026. 	[Title] Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population [Abstract] The Baldwin Effect, first proposed in the late nineteenth century, suggests that the course of evolutionary change can be influenced by individually learned behavior. The existence of this effect is still a hotly debated topic. In this paper clear evidence is presented that learning-based plasticity at the phenotypic level can and does produce directed changes at the genotypic level. This research confirms earlier experimental work done by others, notably Hinton & Nowlan (1987). Further, the amount of plasticity of the learned behavior is shown to be crucial to the size of the Baldwin Effect: either too little or too much and the effect disappears or is significantly reduced. Finally, for learnable traits, the case is made that over many generations it will become easier for the population as a whole to learn these traits (i.e. the phenotypic plasticity of these traits will increase). In this gradual transition from a genetically driven population to one driven by learning, the importance of the Baldwin Effect decreases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1034. 	[Title] Landscapes, Learning Costs and Genetic Assimilation. [Abstract] The evolution of a population can be guided by phenotypic traits acquired by members of that population during their lifetime. This phenomenon, known as the Baldwin Effect, can speed the evolutionary process as traits that are initially acquired become genetically specified in later generations. This paper presents conditions under which this genetic assimilation can take place. As well as the benefits that lifetime adaptation can give a population, there may be a cost to be paid for that adaptive ability. It is the evolutionary trade-off between these costs and benefits that provides the selection pressure for acquired traits to become genetically specified. It is also noted that genotypic space, in which evolution operates, and phenotypic space, on which adaptive processes (such as learning) operate, are, in general, of a different nature. To guarantee an acquired characteristic can become genetically specified, then these spaces must have the property of neighbourhood correlation which means that a small distance between two individuals in phenotypic space implies that there is a small distance between the same two individuals in genotypic space. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105231. 	[Title] Artificial Life as Theoretical Biology: How to do real science with computer simulation [Abstract] Artificial Life (A-Life) research offers, among other things, a new style of computer simulation for understanding biological systems and processes. But most current A-Life work does not show enough methodological sophistication to count as good theoretical biology. As a first step towards developing a stronger methodology for A-Life, this paper (1) identifies some methodological pitfalls arising from the `computer science inuence' in A-Life, (2) suggests some methodological heuristics for A-Life as theoretical biology, (3) notes the strengths of A-Life methods versus previous research methods in biology, (4) examines some open questions in theoretical biology that may benefit from A-Life simulation, and (5) argues that the debate over `Strong A-Life' is not relevant to A-Life's utility for theoretical biology. 1 Introduction: Simulating our way into the Dark Continent Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102550. 	[Title] Culling Teaching -1 Culling and Teaching in Neuro-evolution [Abstract] The evolving population of neural nets contains information not only in terms of genes, but also in the collection of behaviors of the population members. Such information can be thought of as a kind of culture of the population. Two ways of exploiting that culture are explored in this paper: (1) Culling overlarge litters: Generate a large number of offspring with different crossovers, quickly evaluate them by comparing their performance to the population, and throw away those that appear poor. (2) Teaching: Use backpropagation to train offspring toward the performance of the population. Both techniques result in faster, more effective neuro-evolution, and they can be effectively combined, as is demonstrated on the inverted pendulum problem. Additional methods of cultural exploitation are possible and will be studied in future work. These results suggest that cultural exploitation is a powerful idea that allows leveraging several aspects of the genetic algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153945. 	[Title] A study of the effects of group formation on evolutionary search [Abstract]  Please summarise all the given information.

Paper #1129798 has the title `On the relationship between distributed group-behaviour and the behavioural complexity of individuals [Wilson Sober 1994])
` and the abstract ` 
`.
Question: Which category should Paper #1129798 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1129798 has the title `On the relationship between distributed group-behaviour and the behavioural complexity of individuals [Wilson Sober 1994])
` and the abstract ` 
`.
Question: Which category should Paper #1129798 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