Genetic Algorithm Sample Clauses

Genetic Algorithm. The second script contains two variants of a genetic algorithm [Deb12] that have two parameters to tune their behaviour. The variations o er choices of both how the initial population is generated and how parents are selected to produce each subsequent generation. The parameters a ect the size of the initial population and how long the algorithm will continue if no better results are being found. The strength of a closed loop search, such is this genetic search, is that they will perform a search of the design space with- out testing each design and so require less CPU time than an exhaustive search [FGPL17]. This strength comes at the cost of guaranteeing of nding globally optimal designs, the search may instead nd some set of local opti- mums and return those. The following subsections outline the steps take by the genetic algorithm. Initial population generation The rst step in the genetic algorithm is to generate an initial population of designs. The size of this initial set is a parameter the user may set and there is ongoing work that is described in D3.2a [FGPP16] that aims to provide guidance on what this size should be. It is on the generation of this initial set that the two genetic algorithms di er. One version of the script produces an entirely random set of designs and then proceeds to the next step, while the other version attempts to produce a set of designs that is evenly distributed across the design space. Again the experimentation in D3.2a aims to provide guidance about which of these options should be used and when.
Sample 1
AutoNDA by SimpleDocs
Genetic Algorithm. The second script contains two variants of a genetic algorithm [Deb12] that have two parameters to tune their behaviour. The variations offer choices of both how the initial population is generated and how parents are selected to produce each subsequent generation. The parameters affect the size of the initial population and how long the algorithm will continue if no better results are being found. The following subsections outline the steps take by the genetic algorithm. Initial population generation The first step in the genentic algorithm is to generate an initial population of designs. The size of this initial set is a parameter the user may set and there is ongoing work that is described in D3.2a [FGPP16] that aims to provide guidance on what this size should be. It is on the generation of this initial set that the two genetic algorithms differ. One version of the script produces an entirely random set of designs and then proceeds to the next step, while the other version attempts to produce a set of designs that is evenly distributed across the design space. Again the experimentation in D3.2a aims to provide guidance about which of these options should be used and when. Evaluation and ranking The second step in the genetic algorithm is to evaluate the new designs according to the objectives in the DSE config file (section 4.2.2) and then to place them in a partial order according to the ranking defined (section 4.2.4). Progress assessment With the whole population evaluated and ranked it is possible to determine if the fitness of the best designs is improving or not. This is done by looking at the population of the non-dominated set of designs to determine how long, in generations, it has been since the one or more new designs were added to this set. If the number of generations since this set changed is above a threshold then the algorithm assumes that an optimal design has been found and the genetic algorithm halts, returning the graph and table of results to the user. The number of generations the algorithm will proceed to the next step without seeing any improvement is a parameter the user may define for the algorithm and once again this is being investigated so that guidance may be provided. Parent selection and offspring generation If the algorithm decides to proceed, the next step is to select a pair of parents from the whole popula- tion. Here the parents are weighted according to the rank they achieved in the evaluation step such that those ...
Sample 1
Genetic Algorithm. The genetic algorithm that is used in this article is based on algorithms used in Xxxxxxxx et al. [29, 30] and Xxxxxxxxxx et al. [11, 7]. The genetic algorithm for all experiments is a (10, 100) strategy where 100 stands for the number of individuals in the pool and 10 for the number of parents that is selected from that pool. Parents are selected by choosing the ten fittest individuals from the population. Every parent is then copied ten times every generation (making 100 children) and all individuals are mutated using a mutation operator. Unlike previous experiments, no crossover was used in these experiments. Not only have earlier results by Xxxxxxxxxx et al. [11, 7] (see Chapter 4) shown that the impact of crossover on the Majority Problem is minimal, but investigating the effect of self-adaptation is also a lot easier without taking into account crossover. The algorithm uses a comma strategy. This is also different from previous experiments, which used a plus strategy. Preliminary experiments seem to suggest that self-adaptation of mutation rates in GA do not work very well in a plus strategy. This probably has to do with the success rate in the al- gorithm and the fact that the mutation rate only changes if the individual is mutated. In a plus (or elitist) strategy the parents are not mutated and copied to the next generation without any changes. If the success rate in the algorithm goes down, these elite individuals will drown out any form of diversity while at the same time keeping the mutation rate identical and probably too high. Using a plus strategy with self adaptation is probably possible if this effect can be countered somehow and this is worth investigat- ing in the future, but by choosing a comma strategy this problem is evaded. Every surviving individual is mutated every generation, which results in a dynamic mutation rate that evolves at the same time as the individuals object values, thus forcing the algorithm to select the best mutation rate at different stages in the optimization.
Sample 1

Related to Genetic Algorithm

  • Hepatitis B Vaccine Where the Hospital identifies high risk areas where employees are exposed to Hepatitis B, the Hospital will provide, at no cost to the employees, a Hepatitis B vaccine.

  • Influenza Vaccination The parties agree that influenza vaccinations may be beneficial for patients and employees. Upon a recommendation pertaining to a facility or a specifically designated area(s) thereof from the Medical Officer of Health or in compliance with applicable provincial legislation, the following rules will apply:

  • Influenza Vaccine Upon recommendation of the Medical Officer of Health, all employees shall be required, on an annual basis to be vaccinated and or to take antiviral medication for influenza. If the costs of such medication are not covered by some other sources, the Employer will pay the cost for such medication. If the employee fails to take the required medication, she may be placed on an unpaid leave of absence during any influenza outbreak in the home until such time as the employee has been cleared by the public health or the Employer to return to the work environment. The only exception to this would be employees for whom taking the medication will result in the employee being physically ill to the extent that she cannot attend work. Upon written direction from the employee’s physician of such medical condition in consultation with the Employer’s physician, (if requested), the employee will be permitted to access their sick bank, if any, during any outbreak period. If there is a dispute between the physicians, the employee will be placed on unpaid leave. If the employee gets sick as a reaction to the drug and applies for WSIB the Employer will not oppose the application. If an employee is pregnant and her physician believes the pregnancy could be in jeopardy as a result of the influenza inoculation and/or the antiviral medication she shall be eligible for sick leave in circumstances where she is not allowed to attend at work as a result of an outbreak. This clause shall be interpreted in a manner consistent with the Ontario Human Rights Code.

  • Study Population ‌ Infants who underwent creation of an enterostomy receiving postoperative care and awaiting enterostomy closure: to be assessed for eligibility: n = 201 to be assigned to the study: n = 106 to be analysed: n = 106 Duration of intervention per patient of the intervention group: 6 weeks between enterostomy creation and enterostomy closure Follow-up per patient: 3 months, 6 months and 12 months post enterostomy closure, following enterostomy closure (12-month follow-up only applicable for patients that are recruited early enough to complete this follow-up within the 48 month of overall study duration).

  • Screening 3.13.1 Refuse containers located outside the building shall be fully screened from adjacent properties and from streets by means of opaque fencing or masonry walls with suitable landscaping.

  • Random Drug Testing All employees covered by this Agreement shall be subject to random drug testing in accordance with Appendix D.

  • Human Leukocyte Antigen Testing This plan covers human leukocyte antigen testing for A, B, and DR antigens once per member per lifetime to establish a member’s bone marrow transplantation donor suitability in accordance with R.I. General Law §27-20-36. The testing must be performed in a facility that is: • accredited by the American Association of Blood Banks or its successors; and • licensed under the Clinical Laboratory Improvement Act as it may be amended from time to time. At the time of testing, the person being tested must complete and sign an informed consent form that also authorizes the results of the test to be used for participation in the National Marrow Donor program.

  • Treatment Program Testing The Employer may request or require an employee to undergo drug and alcohol testing if the employee has been referred by the employer for chemical dependency treatment or evaluation or is participating in a chemical dependency treatment program under an employee benefit plan, in which case the employee may be requested or required to undergo drug or alcohol testing without prior notice during the evaluation or treatment period and for a period of up to two years following completion of any prescribed chemical dependency treatment program.

  • SAFEGUARDING CHILDREN AND VULNERABLE ADULTS 8.1 The Service Provider will have ultimate responsibility for the management and control of any Regulated Activity provided under this agreement and for the purposes of the Safeguarding Vulnerable Groups Xxx 0000.

  • Population The Population shall be defined as all Paid Claims during the 12-month period covered by the Claims Review.

Time is Money Join Law Insider Premium to draft better contracts faster.