NDLI: Evolution of division of labor in genetically homogenous groups

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Particle swarm optimization with triggered mutation and its implementation based on GPU

Honey-bee dancing-inspired architecture for fault tolerance in wireless sensor networks

Cooperative self-organization in a heterogeneous swarm robotic system

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Ant colony optimization and the minimum cut problem

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Neuronal replicators solve the stability-plasticity dilemma

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Consultant-guided search: a new metaheuristic for combinatorial optimization problems

Beating exhaustive search at its own game: revisiting evolutionary mastermind

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Pruning association rules using statistics and genetic relation algoritm

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NEATfields: evolution of neural fields

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Coevolutionary automated software correction

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Evolution of division of labor in genetically homogenous groups

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Network crossover performance on NK landscapes and deceptive problems

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The maximum hypervolume set yields near-optimal approximation

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Selection mechanisms in memory consideration for examination timetabling with harmony search

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Simultaneous use of different scalarizing functions in MOEA/D

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Coevolutionary multi-population genetic programming for data classification

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Probabilistic performance profiles for the experimental evaluation of stochastic algorithms

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Edge-based representation beats vertex-based representation in shortest path problems

Predicting solution rank to improve performance

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Object-level recombination of commodity applications

Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease

Generalized crowding for genetic algorithms

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Evolution of division of labor in genetically homogenous groups

Content Provider	ACM Digital Library
Author	Ofria, Charles Knoester, David B. Goldsby, Heather J.
Abstract	Within nature, the success of many organisms, including certain species of insects, mammals, slime molds, and bacteria, is attributed to their performance of division of labor, where individuals specialize on specific roles and cooperate to survive. The evolution of division of labor is challenging to study because of the slow pace of biological evolution and imperfect historical data. In this paper, we use digital evolution to evolve groups of clonal organisms that exhibit division of labor. We then investigate what mechanisms they use to perform division of labor (i.e., location awareness or communication) and discover that it varies according to the type of roles being performed. Lastly, we created an environment where groups of organisms needed to complete a set of tasks, but could do so as either generalists or specialists. We varied the costs of switching tasks and determined that increased costs can result in the evolution of division of labor. Moreover, a group used as a case study exhibited both division of labor and cooperative problem decomposition, where members of the group shared partial solutions to solve the full set of problems. This approach has the potential to inform predictions in biological studies, as well as achieving division of labor when using evolutionary computation to solve more complex engineering problems.
Starting Page	135
Ending Page	142
Page Count	8
File Format	PDF
ISBN	9781450300728
DOI	10.1145/1830483.1830507
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Eusociality Digital evolution Avida Division of labor
Content Type	Text
Resource Type	Article

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