Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
“Genotypes” for neural networks
The handbook of brain theory and neural networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Interactive space layout: A graph theoretical approach
DAC '78 Proceedings of the 15th Design Automation Conference
A simple recognition of maximal planar graphs
Information Processing Letters
Compact floor-planning via orderly spanning trees
Journal of Algorithms
Improving Unstructured Peer-to-Peer Systems by Adaptive Connection Establishment
IEEE Transactions on Computers
Two fast tree-creation algorithms for genetic programming
IEEE Transactions on Evolutionary Computation
A Memetic Algorithm for VLSI Floorplanning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Destination-driven routing for low-cost multicast
IEEE Journal on Selected Areas in Communications
Learning polynomial feedforward neural networks by genetic programming and backpropagation
IEEE Transactions on Neural Networks
Architectural space planning using evolutionary computing approaches: a review
Artificial Intelligence Review
An IGA-based design support system for realistic and practical fashion designs
Computer-Aided Design
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The architectural layout design problem, which is concerned with the finding of the best adjacencies between functional spaces among many possible ones under given constraints, can be formulated as a combinatorial optimization problem and can be solved with an Evolutionary Algorithm (EA). We present functional spaces and their adjacencies in form of graphs and propose an EA called EvoArch that works with a graph-encoding scheme. EvoArch encodes topological configuration in the adjacency matrices of the graphs that they represent and its reproduction operators operate on these adjacency matrices. In order to explore the large search space of graph topologies, these reproduction operators are designed to be unbiased so that all nodes in a graph have equal chances of being selected to be swapped or mutated. To evaluate the fitness of a graph, EvoArch makes use of a fitness function that takes into consideration preferences for adjacencies between different functional spaces, budget and other design constraints. By means of different experiments, we show that EvoArch can be a very useful tool for architectural layout design tasks.