In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance.
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization.
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches.
We propose IOHanalyzer, a new software for analyzing the empirical performance of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, and similar optimizers.
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.
We show that it is instead possible to take a meta-learning approach: an ML model of non-trivial Proxies of Human Interpretability (PHIs) can be learned from human feedback, then this model can be incorporated within an ML training process to directly optimize for interpretability.
We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this.
The 0/1 multidimensional knapsack problem is the 0/1 knapsack problem with m constraints which makes it difficult to solve using traditional methods like dynamic programming or branch and bound algorithms.