AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms

17 Sep 20171 code implementation

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.

Layered TPOT: Speeding up Tree-based Pipeline Optimization

18 Jan 20181 code implementation

With the demand for machine learning increasing, so does the demand for tools which make it easier to use.


A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence

COLING 2016 1 code implementation

Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization.


Where are we now? A large benchmark study of recent symbolic regression methods

25 Apr 20181 code implementation

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.

Fast Genetic Algorithms

9 Mar 20172 code implementations

We prove that the $(1+1)$ EA with this heavy-tailed mutation rate optimizes any $\jump_{m, n}$ function in a time that is only a small polynomial (in~$m$) factor above the one stemming from the optimal rate for this $m$.

IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic

8 Jul 20203 code implementations

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.

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

10 Dec 20191 code implementation

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence.

Learning a Formula of Interpretability to Learn Interpretable Formulas

23 Apr 20203 code implementations

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.


Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions

3 Apr 20191 code implementation

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.

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

20 Jul 20191 code implementation

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.