Hierarchical learning with procedural abstraction mechanisms
Hierarchical learning with procedural abstraction mechanisms
Cambrian intelligence: the early history of the new AI
Cambrian intelligence: the early history of the new AI
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Parisian camera placement for vision metrology
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Genetic Programming and Evolvable Machines
A genetic programming approach to automated software repair
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Discovering several robot behaviors through speciation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Semantic building blocks in genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Efficiently evolving programs through the search for novelty
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Rethinking multilevel selection in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
A Multimodal Database for Affect Recognition and Implicit Tagging
IEEE Transactions on Affective Computing
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
The challenges ahead for bio-inspired 'soft' robotics
Communications of the ACM
Geometric semantic genetic programming
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Searching for novel classifiers
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Searching for novel clustering programs
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Evolving a digital multiplier with the pushgp genetic programming system
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Evolving a digital multiplier with the pushgp genetic programming system
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Genetic programming (GP) has proven to be a powerful tool for (semi)automated problem solving in various domains. However, while the algorithmic aspects of GP have been a primary object of study, there is a need to enhance the understanding of the problems where GP is applied. One particular goal is to categorize problems in a meaningful way, in order to select the best tools that can possibly be used to solve them. This paper studies modal problems, a conceptual class of problems recently proposed by Spector at GECCO 2012. Modal problems are those for which a solution program requires different modes of operation for different contexts. The thesis of this paper is that modality, in this sense, is better understood by analyzing program performance in behavioral space. The behavior-based perspective is seen as part of a scale of different forms of analyzing performance; with a coarse view given by a global fitness value and a highly detailed view provided by the semantics approach. On the other hand, behavioral analysis is seen as a flexible approach where the context of a program's performance is considered at in a domain-specific manner. The experimental evidence presented here suggests that behavior-based search could allow a GP to find programs with disjoint behavioral structures, that can satisfy the requirements of each mode of operation of a modal problem.