Randomness conservation inequalities; information and independence in mathematical theories
Information and Control
Active perception and reinforcement learning
Neural Computation
Reinforcement learning in Markovian and non-Markovian environments
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
The evolution of mental models
Advances in genetic programming
Memoryless policies: theoretical limitations and practical results
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Adding temporary memory to ZCS
Adaptive Behavior
Machine Learning - Special issue on inductive transfer
Reinforcement learning with self-modifying policies
Learning to learn
Neuro-Dynamic Programming
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Planning and Acting in Partially Observable Stochastic Domains
Planning and Acting in Partially Observable Stochastic Domains
HQ-Learning: Discovering Markovian Subgoals for Non-Markovian Reinforcement Learning
HQ-Learning: Discovering Markovian Subgoals for Non-Markovian Reinforcement Learning
An analysis of genetic programming
An analysis of genetic programming
Learning Team Strategies: Soccer Case Studies
Machine Learning
Reinforcement Learning Soccer Teams with Incomplete World Models
Autonomous Robots
Sequential Decision Making Based on Direct Search
Sequence Learning - Paradigms, Algorithms, and Applications
Optimal Ordered Problem Solver
Machine Learning
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning computer programs with the bayesian optimization algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Total synthesis of algorithmic chemistries
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Scalability of genetic programming and probabilistic incremental program evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Automatic verilog code generation through grammatical evolution
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot
Genetic Programming and Evolvable Machines
Scalable estimation-of-distribution program evolution
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
Accelerating genetic programming by frequent subtree mining
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Combining cartesian genetic programming with an estimation of distribution algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genetic Programming Crossover: Does It Cross over?
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Program Evolution for General Intelligence
Proceedings of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006
Binary encoding for prototype tree of probabilistic model building GP
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Investigation on evolutionary synthesis of movement commands
Modelling and Simulation in Engineering
Semantic analysis of program initialisation in genetic programming
Genetic Programming and Evolvable Machines
Expert Systems with Applications: An International Journal
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Tightness time for the linkage learning genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Probabilistic developmental program evolution
Proceedings of the 2010 ACM Symposium on Applied Computing
Using instruction matrix based genetic programming to evolve programs
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
A linear estimation-of-distribution GP system
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
On the importance of data balancing for symbolic regression
IEEE Transactions on Evolutionary Computation
Toward an estimation of distribution algorithm for the evolution of artificial neural networks
Proceedings of the Third C* Conference on Computer Science and Software Engineering
Sampling bias in estimation of distribution algorithms for genetic programming using prototype trees
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Semantically-based crossover in genetic programming: application to real-valued symbolic regression
Genetic Programming and Evolvable Machines
Use of infeasible individuals in probabilistic model building genetic network programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Structural difficulty in estimation of distribution genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A parallel evolving algorithm for flexible neural tree
Parallel Computing
EA'05 Proceedings of the 7th international conference on Artificial Evolution
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
Evolve schema directly using instruction matrix based genetic programming
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
An investigation of local patterns for estimation of distribution genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Predict the tertiary structure of protein with flexible neural tree
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Revisiting the Memory of Evolution
Fundamenta Informaticae
A Historical Perspective on the Evolution of Executable Structures
Fundamenta Informaticae
Swarm optimization and Flexible Neural Tree for microarray data classification
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Introducing graphical models to analyze genetic programming dynamics
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Hi-index | 0.00 |
Probabilistic incremental program evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions, population-based incremental learning, and tree-coded programs like those used in some variants of genetic programming (GP). PIPE iteratively generates successive populations of functional programs according to an adaptive probability distribution over all possible programs. Each iteration, it uses the best program to refine the distribution. Thus, it stochastically generates better and better programs. Since distribution refinements depend only on the best program of the current population, PIPE can evaluate program populations efficiently when the goal is to discover a program with minimal runtime. We compare PIPE to GP on a function regression problem and the 6-bit parity problem. We also use PIPE to solve tasks in partially observable mazes, where the best programs have minimal runtime.