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.

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION

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.

DOCUMENT SUMMARIZATION 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.

META-LEARNING

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.