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Found 22 papers, 9 papers with code
Leveraging Symbolic Regression for Heuristic Design in the Traveling Thief Problem
The Traveling Thief Problem is an NP-hard combination of the well known traveling salesman and knapsack packing problems.
DALex: Lexicase-like Selection via Diverse Aggregation
Lexicase selection has been shown to provide advantages over other selection algorithms in several areas of evolutionary computation and machine learning.
Optimizing Neural Networks with Gradient Lexicase Selection
Lexicase selection is an uncompromising method developed in evolutionary computation, which selects models on the basis of sequences of individual training case errors instead of using aggregated metrics such as loss and accuracy.
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance
Furthermore, we find that this technique results in competitive performance on the diversity-focused metrics of QD-Score and Coverage, without explicitly optimizing for these things.
Quality Diversity through Human Feedback
Meanwhile, Quality Diversity (QD) algorithms excel at identifying diverse and high-quality solutions but often rely on manually crafted diversity metrics.
Particularity
We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges.
Probabilistic Lexicase Selection
Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning.
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?
This means that diversity is important to help us reach an objective, but is not an objective in itself.
Analyzing the Interaction Between Down-Sampling and Selection
We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection.
A Static Analysis of Informed Down-Samples
We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection.
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving
Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions.
Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
Recent works show that parameterized quantum circuits (PQCs) can be used to solve challenging reinforcement learning (RL) tasks with provable learning advantages.
Lexicase Selection at Scale
Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream.
Functional Code Building Genetic Programming
General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally.
The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase Selection
Although this down-sampling procedure has been shown to significantly improve performance across a variety of problems, it does not seem to do so due to encouraging adaptability through environmental change.
Evolving Neural Selection with Adaptive Regularization
We propose the Adaptive Neural Selection (ANS) framework, which evolves to weigh neurons in a layer to form network variants that are suitable to handle different input cases.
Problem-solving benefits of down-sampled lexicase selection
Lexicase selection, by contrast, selects on the basis of performance on random sequences of training cases; this has been shown to enhance problem-solving power in many circumstances.
Code Building Genetic Programming
In recent years the field of genetic programming has made significant advances towards automatic programming.
Lexicase selection in Learning Classifier Systems
We show that batch-lexicase selection results in the creation of more generic rules which is favorable for generalization on future data.
Epsilon-Lexicase Selection for Regression
We run a series of experiments on real-world and synthetic problems with several treatments of epsilon and quantify how epsilon affects parent selection and model performance.
Lexicase Selection of Specialists
Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than the best error in the selection pool, until a single individual remains.
A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection.
