The latest book on Genetic Programming, Poli, Langdon and McPhee's (with contributions
from John R. Koza) A Field Guide to Genetic Programming represents an exciting landmark …

We investigate the effects of semantically-based crossover operators in genetic
programming, applied to real-valued symbolic regression problems. We propose two new …

Empirical modeling, which is a process of developing a mathematical model of a system
from experimental data, has attracted many researchers due to its wide applicability. Finding …

This study proposes a technique aimed at the automatic search for parameter adaptation
strategies in a differential evolution algorithm with genetic programming symbolic …

Mathematical formulas are the crystallization of human wisdom in exploring the laws of
nature for thousands of years. Describing the complex laws of nature with a concise …

In this paper, a new genetic programming (GP) algorithm for symbolic regression problems
is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical …

The goal of having computers automatically solve problems is central to artificial
intelligence, machine learning, and the broad area encompassed by what Turing called …

Estimation of the epidemiology curve for the COVID-19 pandemic can be a very
computationally challenging task. Thus far, there have been some implementations of …

Empirically, symbolic regression tries to identify, through genetic programming and within
the sphere of mathematical expressions, a model which best explains the relationship …

Learning efficient and interpretable policies has been a challenging task in reinforcement
learning (RL), particularly in the visual RL setting with complex scenes. While neural …