Search
Search Results
-
Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic scheduling
Linear genetic programming (LGP) is a genetic programming paradigm based on a linear sequence of instructions being executed. An LGP individual can...
-
Phenotype Search Trajectory Networks for Linear Genetic Programming
In this study, we visualise the search trajectories of a genetic programming system as graph-based models, where nodes are genotypes/phenotypes and...
-
Spatial Genetic Programming
An essential characteristic of brains in intelligent organisms is their spatial organization, in which different parts of the brain are responsible...
-
An ensemble learning interpretation of geometric semantic genetic programming
Geometric semantic genetic programming (GSGP) is a variant of genetic programming (GP) that directly searches the semantic space of programs to...
-
Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming
We present SLUG, a recent method that uses genetic algorithms as a wrapper for genetic programming and performs feature selection while inducing...
-
Linear programming-based multi-objective floorplanning optimization for system-on-chip
In the area of very large-scale integrated circuit design, optimizing floorplans for area, wirelength, and temperature poses a daunting challenge....
-
Naturally Interpretable Control Policies via Graph-Based Genetic Programming
In most high-risk applications, interpretability is crucial for ensuring system safety and trust. However, existing research often relies on...
-
An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling
Dynamic job shop scheduling has a wide range of applications in reality such as order picking in warehouse. Using genetic programming to design...
-
Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search
Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses...
-
GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programming
Geometric Semantic Genetic Programming (GSGP) proposed an important enhancement to GP-based learning, incorporating search operators that operate...
-
Using Genetic Programming and Linear Regression for Academic Performance Analysis
The academic evaluation process, even today, is the subject of much discussion. This process can use quantitative analysis to indicate the level of...
-
GPAM: Genetic Programming with Associative Memory
We focus on the evolutionary design of programs capable of capturing more randomness and outliers in the input data set than the standard genetic...
-
A Comparative Study of Genetic Programming Variants
Genetic programming tends to optimize complicated structures producing human-competitive results; therefore, it is applied to a wide range of...
-
A Genetic Programming Encoder for Increasing Autoencoder Interpretability
Autoencoders are powerful models for non-linear dimensionality reduction. However, their neural network structure makes it difficult to interpret how...
-
Genetic Programming
GAs, studied in Chap. 3 , are capable of solving many problems and simple enough to allow for solid...
-
Geometric semantic genetic programming with normalized and standardized random programs
Geometric semantic genetic programming (GSGP) represents one of the most promising developments in the area of evolutionary computation (EC) in the...
-
Cellular geometric semantic genetic programming
Among the different variants of Genetic Programming (GP), Geometric Semantic GP (GSGP) has proved to be both efficient and effective in finding good...
-
Evolutionary feature selection approaches for insolvency business prediction with genetic programming
This study uses different feature selection methods in the field of business failure prediction and tests the capability of Genetic Programming (GP)...
-
Memetic Semantic Genetic Programming for Symbolic Regression
This paper describes a new memetic semantic algorithm for symbolic regression (SR). While memetic computation offers a way to encode domain knowledge...
-
Semantic segmentation network stacking with genetic programming
Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding....
