Automatic Design of a Hyper-Heuristic Framework With Gene Expression Programming for Combinatorial Optimization Problems
Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high-level heuristic (heuristic selection ...
Semantic Backpropagation for Designing Search Operators in Genetic Programming
In genetic programming, a search algorithm is expected to produce a program that achieves the desired final computation state (desired output). To reach that state, an executing program needs to traverse certain intermediate computation states. An ...
Tuning Optimization Algorithms Under Multiple Objective Function Evaluation Budgets
Most sensitivity analysis studies of optimization algorithm control parameters are restricted to a single objective function evaluation (OFE) budget. This restriction is problematic because the optimality of control parameter values (CPVs) is dependent ...
Analysis of Cartesian Genetic Programming’s Evolutionary Mechanisms
Understanding how search operators interact with solution representation is a critical step to improving search. In Cartesian genetic programming (CGP), and genetic programming (GP) in general, the complex genotype to phenotype map makes achieving this ...
An Effective Method for Evolving Reaction Networks in Synthetic Biochemical Systems
In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses ...
Social Networks and Asset Price Dynamics
In this paper, we investigate how behavioral contagion in terms of mimetic strategy learning within a social network would affect the asset price dynamics. The characteristics of this paper are as follows. First, traders are characterized by bounded ...
An Algorithm for Many-Objective Optimization With Reduced Objective Computations: A Study in Differential Evolution
In this paper we have developed an algorithm for many-objective optimization problems, which will work more quickly than existing ones, while offering competitive performance. The algorithm periodically reorders the objectives based on their conflict ...
Locating Multiple Optimal Solutions of Nonlinear Equation Systems Based on Multiobjective Optimization
Nonlinear equation systems may have multiple optimal solutions. The main task of solving nonlinear equation systems is to simultaneously locate these optimal solutions in a single run. When solving nonlinear equation systems by evolutionary algorithms, ...
Evolutionary Approach to Approximate Digital Circuits Design
In approximate computing, the requirement of perfect functional behavior can be relaxed because some applications are inherently error resilient. Approximate circuits, which fall into the approximate computing paradigm, are designed in such a way that ...
A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization
Decomposition-based evolutionary algorithms have been quite successful in solving optimization problems involving two and three objectives. Recently, there have been some attempts to exploit the strengths of decomposition-based approaches to deal with ...