Genetic Programming with Local Search to evolve priority rules for scheduling jobs on a machine with time-varying capacity

AbstractIn this paper we analyze the effects of using nonlinear least squares for parameter identification of symbolic regression models and integrate it as local search mechanism in tree-based genetic programming. We employ the Levenberg–Marquardt algorithm for parameter optimization and calculate gradients via automatic differentiation. We provide examples where the parameter identification succeeds and fails and highlight its computational overhead. Using an extensive suite of symbolic regression benchmark problems we demonstrate the increased performance when incorporating nonlinear least squares within genetic programming. Our results are compared with recently published results obtained by several genetic programming variants and state of the art machine learning algorithms. Genetic programming with nonlinear least squares performs among the best on the defined benchmark suite and the local search can be easily integrated in different genetic programming algorithms as long as only differentiable functions are used within the models.

Download Full-text

Dow Jones Index Return Forecasting: Semantics Based Genetic Programming with Local Search Optimizer

International Journal of Bio-Inspired Computation ◽

10.1504/ijbic.2017.10004325 ◽

2017 ◽

Vol 10 (3) ◽

pp. 1

Author(s):

Ales Popovic ◽

Leonardo Trujillo ◽

Leonardo Vanneschi ◽

Mauro Castelli

Keyword(s):

Genetic Programming ◽

Local Search ◽

Jones Index

Download Full-text

An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem

Omega ◽

10.1016/j.omega.2015.03.007 ◽

2015 ◽

Vol 56 ◽

pp. 88-98 ◽

Cited By ~ 42

Author(s):

Nasser R. Sabar ◽

Graham Kendall

Keyword(s):

Local Search ◽

Iterated Local Search ◽

Time Varying ◽

Aircraft Landing

Download Full-text

An Iterated Local Search Algorithm for a Place Scheduling Problem

Mathematical Problems in Engineering ◽

10.1155/2013/659297 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7

Author(s):

Shicheng Hu ◽

Zhaoze Zhang ◽

Qingsong He ◽

Xuedong Sun

Keyword(s):

Local Search ◽

Schedule Problem ◽

Search Algorithm ◽

Heuristic Method ◽

Three Dimensional ◽

Iterated Local Search ◽

Scheduling Problem ◽

Priority Rules ◽

Resource Schedule ◽

Best Fit

We study the place scheduling problem which has many application backgrounds in realities. For the block manufacturing project with special manufacturing platform requirements, we propose a place resource schedule problem. First, the mathematical model for the place resource schedule problem is given. On the basis of resource-constrained project scheduling problem and packing problem, we develop a hybrid heuristic method which combines priority rules and three-dimensional best fit algorithm, in which the priority rules determine the scheduling order and the three-dimensional best fit algorithm solves the placement. After this method is used to get an initial solution, the iterated local search is employed to get an improvement. Finally, we use a set of simulation data to demonstrate the steps of the proposed method and verify its feasibility.

Download Full-text