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Changing the genospace: Solving ga problems with cartesian genetic programming
 In Proc. of EuroGP 2007
, 2007
"... Abstract. Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) capable of acquiring, evolving and reusing partial solutions. In this paper, we apply for the first time CGP and ECGP to the onesmax and order3 deceptive problems, which are normally ass ..."
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Cited by 3 (1 self)
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Abstract. Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) capable of acquiring, evolving and reusing partial solutions. In this paper, we apply for the first time CGP and ECGP to the onesmax and order3 deceptive problems, which are normally
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 524 (4 self)
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the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Paretooptimal points, instead of a single point. Since genetic algorithms(GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a
Explicit Maintenance of Genetic Diversity on Genospaces
, 1994
"... When evolving genotypes, i.e. structures, with an evolutionary algorithm (EA), e.g. genetic programming (GP), genetic diversity, i.e. structural diversity, of each generation is a necessary condition for the fast detection of a highfitness individual and for a fast adaptation of the population to ..."
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Cited by 12 (1 self)
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When evolving genotypes, i.e. structures, with an evolutionary algorithm (EA), e.g. genetic programming (GP), genetic diversity, i.e. structural diversity, of each generation is a necessary condition for the fast detection of a highfitness individual and for a fast adaptation of the population
Cartesian Genetic Programming
, 2000
"... This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node funct ..."
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Cited by 228 (58 self)
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This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node
Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
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Cited by 610 (15 self)
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The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified
Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention
 Psychological Bulletin
, 1992
"... The authors suggest that the most promising route to effective strategies for the prevention of adolescent alcohol and other drug problems is through a riskfocused approach. This approach requires the identification of risk factors for drug abuse, identification of methods by which risk factors hav ..."
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Cited by 693 (18 self)
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The authors suggest that the most promising route to effective strategies for the prevention of adolescent alcohol and other drug problems is through a riskfocused approach. This approach requires the identification of risk factors for drug abuse, identification of methods by which risk factors
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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fields, including bioinformatics, communication theory, statistical physics, combinatorial optimization, signal and image processing, information retrieval and statistical machine learning. Many problems that arise in specific instances — including the key problems of computing marginals and modes
Evolving Neural Networks through Augmenting Topologies
 Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task ..."
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Cited by 524 (113 self)
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An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
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Cited by 1108 (51 self)
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This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensorbased planning, visibility, decisiontheoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edg ..."
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Cited by 1276 (124 self)
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A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collisionfree configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)
Results 1  10
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