Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic programming using a minimum description length principle
Advances in genetic programming
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Genome Length as an Evolutionary Self-adaptation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Survey of Intron Research in Genetics
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Empirical studies of the genetic algorithm with noncoding segments
Evolutionary Computation
A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Duplication of coding segments in genetic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
The Proportional Genetic Algorithm: Gene Expression in a Genetic Algorithm
Genetic Programming and Evolvable Machines
Fighting Bloat with Nonparametric Parsimony Pressure
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Modification point depth and genome growth in genetic programming
Evolutionary Computation
Problem Difficulty and Code Growth in Genetic Programming
Genetic Programming and Evolvable Machines
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Emergence of genomic self-similarity in location independent representations
Genetic Programming and Evolvable Machines
A comparison of bloat control methods for genetic programming
Evolutionary Computation
Source routing in the internet with reinforcement learning and genetic algorithms
SEPADS'06 Proceedings of the 5th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems
Gene regulation in a particle metabolome
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Adaptation of length in a nonstationary environment
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Simultaneous optimization of weights and structure of an RBF neural network
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Self-adaptation of genome size in artificial organisms
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Synapsing variable length crossover: an algorithm for crossing and comparing variable length genomes
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Symbiogenesis as a mechanism for building complex adaptive systems: a review
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Designing a morphogenetic system for evolvable hardware
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
GEARNet: grammatical evolution with artificial regulatory networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. The result is a system called Virtual Virus (VIV). VIV incorporates a number of more biologically plausible mechanisms, including a more flexible genotype-to-phenotype mapping. In VIV the genes are independent of position, and genomes can vary in length and may contain noncoding regions, as well as duplicative or competing genes. Initial computational studies with VIV have already revealed several emergent phenomena of both biological and computational interest. In the absence of any penalty based on genome length, VIV develops individuals with long genomes and also performs more poorly (from a problem-solving viewpoint) than when a length penalty is used. With a fixed linear length penalty, genome length tends to increase dramatically in the early phases of evolution and then decrease to a level based on the mutation rate. The plateau genome length (i.e., the average length of individuals in the final population) generally increases in response to an increase in the base mutation rate. When VIV converges, there tend to be many copies of good alternative genes within the individuals. We observed many instances of switching between active and inactive genes during the entire evolutionary process. These observations support the conclusion that noncoding regions serve as scratch space in which VIV can explore alternative gene values. These results represent a positive step in understanding how GAs might exploit more of the power and flexibility of biological evolution while simultaneously providing better tools for understanding evolving biological systems.