Genetic Algorithm crossover strategy for enhanced solution space exploration

1998 ◽  
Author(s):  
Kurt Anderson ◽  
YuHung Hsu
Keyword(s):  
Genetic Algorithm ◽  
Space Exploration ◽  
Solution Space

Application of a Genetic Algorithm to Concept Variant Selection

2006 ◽  
Author(s):  
Ryan S. Hutcheson ◽  
Robert L. Jordan ◽  
Robert B. Stone ◽  
Janis P. Terpenny ◽  
Xiaomeng Chang
Keyword(s):  
Genetic Algorithm ◽  
Product Development ◽  
Solution Space ◽  
Variant Selection ◽  
Functional Modeling ◽  
Detailed Design ◽  
Modeling Techniques ◽  
Break Even Point ◽  
Future Work ◽  
Selection Of

This paper outlines a framework for applying a genetic algorithm to the selection of component variants between the conceptual and detailed design stages of product development. A genetic algorithm (GA) is defined for the problem and an example is presented that demonstrates its application and usefulness. Functional modeling techniques are used to formulate the design problem and generate the chromosomes that are evaluated with the algorithm. In the presented example, suitable GA parameters and the break-even point where the GA surpassed an enumerated search of the same solution space were found. Recommend uses of the GA along with limitations of the method and future work are presented as well.

Grid Platform Applied to the Vehicle Routing Problem with Time Windows for the Distribution of Products

2012 ◽  
pp. 52-81
Author(s):  
Marco Antonio Cruz-Chávez ◽  
Abelardo Rodríguez-León ◽  
Rafael Rivera-López ◽  
Fredy Juárez-Pérez ◽  
Carmen Peralta-Abarca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Grid Computing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Solution Space ◽  
Routing Problem ◽  
Computing Platform ◽  
Hard Problems ◽  
Computing Platforms

Around the world there have recently been new and more powerful computing platforms created that can be used to work with computer science problems. Some of these problems that are dealt with are real problems of the industry; most are classified by complexity theory as hard problems. One such problem is the vehicle routing problem with time windows (VRPTW). The computational Grid is a platform which has recently ventured into the treatment of hard problems to find the best solution for these. This chapter presents a genetic algorithm for the vehicle routing problem with time windows. The algorithm iteratively applies a mutation operator, first of the intelligent type and second of the restricting type. The algorithm takes advantage of Grid computing to increase the exploration and exploitation of the solution space of the problem. The Grid performance is analyzed for a genetic algorithm and a measurement of the latencies that affect the algorithm is studied. The convenience of applying this new computing platform to the execution of algorithms specially designed for Grid computing is presented.

scholarly journals A Computational Study of the Effect of Experience on Problem/Solution Space Exploration in Teams

2019 ◽  
Vol 1 (1) ◽  
pp. 11-20
Author(s):  
Marija Majda Perisic ◽  
Tomislav Martinec ◽  
Mario Storga ◽  
John S Gero
Keyword(s):  
Computational Study ◽  
Space Exploration ◽  
Solution Space ◽  
Problem Solution ◽  
Design Team ◽  
Design Teams ◽  
Agent Based Model ◽  
Problem Space ◽  
Agent Based ◽  
The Impact

AbstractThis paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results.

Design Space Exploration Using UTNoCs and Genetic Algorithm

2016 ◽  
Author(s):  
Jonathan Wanderley De Mesquita ◽  
Marcos Oliveira da Cruz ◽  
Monica Magalhaes Pereira ◽  
Marcio E. Kreutz
Keyword(s):  
Genetic Algorithm ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration

Analysis of Pavement Management Activities Programming by Genetic Algorithms

1998 ◽  
Vol 1643 (1) ◽  
pp. 1-6 ◽  
Author(s):  
T. F. Fwa ◽  
W. T. Chan ◽  
K. Z. Hoque
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Solution Space ◽  
Pavement Management ◽  
Practical Significance ◽  
Optimization Analysis ◽  
Large Solution ◽  
Pavement Management System ◽  
Pavement Maintenance ◽  
Operational Characteristics

The application of genetic algorithms to programming of pavement maintenance activities at the network level is demonstrated. The operational characteristics of the genetic algorithm technique and its relevance to solving the programming problem in a Pavement Management System (PMS) are discussed. The robust search capability of genetic algorithms enables them to effectively handle the highly constrained problem of pavement management activities programming, which has an extremely large solution space of astronomical scale. Examples are presented to highlight the versatility of genetic algorithms in accommodating different objective function forms. This versatility makes the algorithms an effective tool for planning in PMS. It is also demonstrated that composite objective functions that combine two or more different objectives can be easily considered without having to reformulate the genetic algorithm computer program. Another useful feature of genetic algorithm solutions is the availability of near-optimal solutions besides the "best" solution. This has practical significance as it gives the users the flexibility to examine the suitability of each solution when practical constraints and factors not included in the optimization analysis are considered.

Modeling and Optimization for Steel Grade Selection Based on Hierarchical Genetic Algorithm

2012 ◽  
Vol 505 ◽  
pp. 203-208
Author(s):  
Jian Yi ◽  
Bin Du ◽  
Qiang Liu ◽  
Yun Lin ◽  
Ke Wei Huang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Difficult Problem ◽  
Steel Grade ◽  
Reasonable Time ◽  
Steel Making ◽  
Steel Grades ◽  
The Mathematical Model ◽  
Hierarchical Genetic Algorithm

In steel-making process, when a furnace is charged, there are many optional steel grades for each slab. It is a difficult problem to select the appropriate steel grade for each slab. In this paper, based on the analysis of technics constraints in steel-making process, the steel grade intensivism problem is described, and the mathematical model is also established. To solve the above problem, a newly designed hierarchical genetic algorithm is proposed, where the hierarchical manner is used to decrease the solution space. The effectiveness of the approach is demonstrated by a simulation. The optimal solution can be obtained in reasonable time, which will be helpful to decrease the scraps between two steel grades while casting, to decrease the sum of surplus, and eventually to cut down the stock.

scholarly journals Design of Non-Anthropomorphic Robotic Hands for Anthropomorphic Tasks

2011 ◽  
Author(s):  
Edgar Simo-Serra ◽  
Francesc Moreno-Noguer ◽  
Alba Perez-Gracia
Keyword(s):  
Genetic Algorithm ◽  
Solution Space ◽  
Kinematic Chain ◽  
Human Hand ◽  
Dimensional Synthesis ◽  
Robotic Hands ◽  
Serial Chain ◽  
Finite Set ◽  
Numerical Solver ◽  
Local Optimizer

In this paper, we explore the idea of designing non-anthropomorphic multi-fingered robotic hands for tasks that replicate the motion of the human hand. Taking as input data a finite set of rigid-body positions for the five fingertips, we develop a method to perform dimensional synthesis for a kinematic chain with a tree structure, with five branches that share three common joints. We state the forward kinematics equations of relative displacements for each serial chain expressed as dual quaternions, and solve for up to five chains simultaneously to reach a number of positions along the hand trajectory. This is done using a hybrid global numerical solver that integrates a genetic algorithm and a Levenberg-Marquardt local optimizer. Although the number of candidate solutions in this problem is very high, the use of the genetic algorithm allows us to perform an exhaustive exploration of the solution space to obtain a set of solutions. We can then choose some of the solutions based on the specific task to perform. Note that these designs match the task exactly while generally having a finger design radically different from that of the human hand.

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