Semantic linear genetic programming for symbolic regression
Symbolic regression (SR) is an important problem with many applications, such as
automatic programming tasks and data mining. Genetic programming (GP) is a commonly …
automatic programming tasks and data mining. Genetic programming (GP) is a commonly …
Multitask linear genetic programming with shared individuals and its application to dynamic job shop scheduling
Multitask genetic programming methods have been applied to various domains, such as
classification, regression, and combinatorial optimization problems. Most existing multitask …
classification, regression, and combinatorial optimization problems. Most existing multitask …
A generic construction for crossovers of graph-like structures and its realization in the eclipse modeling framework
In model-driven optimization (MDO), domain-specific models are used to define and solve
optimization problems via meta-heuristic search, often via evolutionary algorithms. Models …
optimization problems via meta-heuristic search, often via evolutionary algorithms. Models …
Investigation of linear genetic programming for dynamic job shop scheduling
Using genetic programming-based hyper-heuristic methods to automatically design
dispatching rules has become one of the most effective methods to solve dynamic job shop …
dispatching rules has become one of the most effective methods to solve dynamic job shop …
Efficient ontology matching through compact linear genetic programming with surrogate-assisted local search
X Xue, JCW Lin, T Su - Swarm and Evolutionary Computation, 2024 - Elsevier
Ontology is a foundational technique of Semantic Web, which enables meaningful
interpretation of Web data. However, ontology heterogeneity obstructs the communications …
interpretation of Web data. However, ontology heterogeneity obstructs the communications …
A review of bio-inspired algorithms as image processing techniques
NE Abdul Khalid, N Md Ariff, S Yahya… - … and Computer Systems …, 2011 - Springer
This paper reviews 80 out of 130 bio-inspired Algorithm (BIAs) researches published in
google scholar and IEEExplore between the periods of 1995 to 2010 used to solve image …
google scholar and IEEExplore between the periods of 1995 to 2010 used to solve image …
Graph-based linear genetic programming: a case study of dynamic scheduling
Linear genetic programming (LGP) has been successfully applied to various problems such
as classification, symbolic regression and hyper-heuristics for automatic heuristic design. In …
as classification, symbolic regression and hyper-heuristics for automatic heuristic design. In …
A further investigation to improve linear genetic programming in dynamic job shop scheduling
Dynamic Job Shop Scheduling (DJSS) is an important problem with many real-world
applications. Genetic programming is a promising technique to solve DJSS, which …
applications. Genetic programming is a promising technique to solve DJSS, which …
A generic construction for crossovers of graph-like structures
In model-driven optimization (MDO), domain-specific models are used to define and solve
optimization problems with evolutionary algorithms. Models are typically evolved using …
optimization problems with evolutionary algorithms. Models are typically evolved using …
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 be decoded into a directed …
sequence of instructions being executed. An LGP individual can be decoded into a directed …