The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Visualization of evolutionary computation processes from a population perspective
Intelligent Data Analysis
A Visualization of Genetic Algorithm Using the Pseudo-color
Neural Information Processing
What Hides in Dimension X? A Quest for Visualizing Particle Swarms
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
VISPLORE: a toolkit to explore particle swarms by visual inspection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Visualizing the search process of particle swarm optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Visualisation of building blocks in evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
New usage of Sammon's mapping for genetic visualization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
EvoShelf: a system for managing and exploring evolutionary data
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Tracer spectrum: a visualisation method for distributed evolutionary computation
Genetic Programming and Evolvable Machines
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Visualization for genetic evolution of target movement in battle fields
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Search based software engineering
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Visual analysis of population scatterplots
EA'11 Proceedings of the 10th international conference on Artificial Evolution
GraphEA: a 3D educational tool for genetic algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper surveys the state of the art in evolutionary algorithm visualization and describes a new tool called GAVEL. It provides a means to examine in a genetic algorithm (GA) how crossover and mutation operations assembled the final result, where each of the alleles came from, and a way to trace the history of user-selected sets of alleles. A visualization tool of this kind can be very useful in choosing operators and parameters and in analyzing how and, indeed, whether or not a GA works. We describe the new tool and illustrate some of the benefits that can be gained from using it with reference to three different problems: a timetabling problem, a job-shop scheduling problem, and Goldberg and Horn's long-path problem. We also compare the tool to other available visualization tools, pointing out those features which are novel and identifying complementary features in other tools