Evolutionary Optimization Using Graph Based Evolutionary Algorithms

2003 ◽  
Author(s):  
Steven M. Corns ◽  
Kenneth M. Bryden ◽  
Daniel A. Ashlock
Keyword(s):  
Evolutionary Algorithms ◽  
Population Size ◽  
Fitness Landscape ◽  
Optimization Technique ◽  
Evolutionary Process ◽  
Evolutionary Optimization ◽  
Genetic Characteristics ◽  
Rate Of Spread ◽  
Optimal Population ◽  
The Impact

Graph based evolutionary algorithms (GBEAs) are a novel evolutionary optimization technique that utilize population graphing to impose a topology or geography on the evolving solution set. In many cases in nature, the ability of a particular member of a population to mate and reproduce is limited. The factors creating these limits vary widely and include geographical distance, mating rituals, and others. The effect of these factors is to limit the mating pool, reducing the rate of spread of genetic characteristics, and increased diversity within the population. GBEAs mimic these factors resulting in increased diversity within the solution population. When properly tuned to the problem and the size of the population set, GBEAs can result in improved convergence times and a more diverse number of viable solutions. This paper examines the impact of the fitness landscape, population size, and choice of graph on the evolutionary process. In general, it was found that there was an optimal population size and graph combination for each problem. This optimal graph/population was problem dependent.

FUNCTION GRANULARITY ESTIMATION FOR MULTIMODAL OPTIMIZATION

2005 ◽  
Vol 14 (01n02) ◽  
pp. 199-213 ◽  
Author(s):  
JIAN ZHANG ◽  
XIAOHUI YUAN ◽  
BILL P. BUCKLES
Keyword(s):  
Population Size ◽  
Resource Sharing ◽  
Fitness Landscape ◽  
Function Optimization ◽  
Pac Learning ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
Optimal Population ◽  
Probably Approximately Correct ◽  
Fitness Distance Correlation

In this article, we study the subspace function granularity and present a method to estimate the sharing distance and the optimal population size. To achieve multimodal function optimization, niching techniques diversify the population of Evolutionary Algorithms (EA) and encourage heterogeneous convergence to multiple optima. The key to a successful diversification is effective resource sharing. Without knowing the fitness landscape, resource sharing is usually determined by uninformative assumptions on the number of peaks. Using the Probably Approximately Correct (PAC) learning theory and the ∊-cover concept, a PAC neighborhood for a set of samples is derived. Within this neighborhood, we sample the fitness landscape and compute the subspace Fitness Distance Correlation (FDC) coefficients. Using the estimated granularity feature of the fitness landscape, the sharing distance and the population size are determined. Experiments demonstrate that by using the estimated population size and sharing distance an Evolutionary Algorithm successfully identifies multiple optima.

scholarly journals Productive fitness in diversity-aware evolutionary algorithms

2021 ◽  
Author(s):  
Thomas Gabor ◽  
Thomy Phan ◽  
Claudia Linnhoff-Popien
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Process ◽  
Evolutionary Optimization ◽  
Optimization Process ◽  
A Posteriori ◽  
Evolutionary Path ◽  
Trade Off ◽  
Exploration Exploitation ◽  
Insight Into ◽  
The Ideal

AbstractIn evolutionary algorithms, the notion of diversity has been adopted from biology and is used to describe the distribution of a population of solution candidates. While it has been known that maintaining a reasonable amount of diversity often benefits the overall result of the evolutionary optimization process by adjusting the exploration/exploitation trade-off, little has been known about what diversity is optimal. We introduce the notion of productive fitness based on the effect that a specific solution candidate has some generations down the evolutionary path. We derive the notion of final productive fitness, which is the ideal target fitness for any evolutionary process. Although it is inefficient to compute, we show empirically that it allows for an a posteriori analysis of how well a given evolutionary optimization process hit the ideal exploration/exploitation trade-off, providing insight into why diversity-aware evolutionary optimization often performs better.

scholarly journals Inferring long-term effective population size with Mutation-Selection models

2021 ◽  
Author(s):  
T. Latrille ◽  
V. Lanore ◽  
N. Lartillot
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Fitness Landscape ◽  
Evolutionary Process ◽  
Simulated Data ◽  
Effective Population ◽  
Current Form ◽  
Codon Models ◽  
Extant Species

AbstractMutation-selection phylogenetic codon models are grounded on population genetics first principles and represent a principled approach for investigating the intricate interplay between mutation, selection and drift. In their current form, mutation-selection codon models are entirely characterized by the collection of site-specific amino-acid fitness profiles. However, thus far, they have relied on the assumption of a constant genetic drift, translating into a unique effective population size (Ne) across the phylogeny, clearly an unreasonable hypothesis. This assumption can be alleviated by introducing variation in Ne between lineages. In addition to Ne, the mutation rate (μ) is susceptible to vary between lineages, and both should co-vary with life-history traits (LHTs). This suggests that the model should more globally account for the joint evolutionary process followed by all of these lineage-specific variables (Ne, μ, and LHTs). In this direction, we introduce an extended mutation-selection model jointly reconstructing in a Bayesian Monte Carlo framework the fitness landscape across sites and long-term trends in Ne, μ and LHTs along the phylogeny, from an alignment of DNA coding sequences and a matrix of observed LHTs in extant species. The model was tested against simulated data and applied to empirical data in mammals, isopods and primates. The reconstructed history of Ne in these groups appears to correlate with LHTs or ecological variables in a way that suggests that the reconstruction is reasonable, at least in its global trends. On the other hand, the range of variation in Ne inferred across species is surprisingly narrow. This last point suggests that some of the assumptions of the model, in particular concerning the assumed absence of epistatic interactions between sites, are potentially problematic.

Toward a Theory of Evolution Strategies: On the Benefits of Sex— the (μ/μ, λ) Theory

1995 ◽  
Vol 3 (1) ◽  
pp. 81-111 ◽  
Author(s):  
Hans-Georg Beyer
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Population Size ◽  
Building Block ◽  
Space Dimension ◽  
Spherical Model ◽  
Dimensional Parameter ◽  
Parameter Spaces ◽  
Theory Of Evolution ◽  
Optimal Population

The multirecombinant (μ/μ, λ) evolution strategy (ES) is investigated for real-valued, N-dimensional parameter spaces. The analysis includes both intermediate recombination and dominant recombination, as well. These investigations are done for the spherical model first. The problem of the optimal population size depending on the parameter space dimension N is solved. A method extending the results obtained for the spherical model to nonspherical success domains is presented. The power of sexuality is discussed and it is shown that this power does not stem mainly from the “combination” of “good properties” of the mates (building block hypothesis) but rather from genetic repair diminishing the influence of harmful mutations. The dominant recombination is analyzed by introduction of surrogate mutations leading to the concept of species. Conclusions for evolutionary algorithms (EAs), including genetic algorithms (GAs), are drawn.

Clinical application of polymorphic variants of the genes ESR1 and CYP2D6*4 in patients with breast and endometrial cancer

2017 ◽  
pp. 132-138
Author(s):  
O.V. Paliychuk ◽  
◽  
L.Z. Polishchuk ◽  
Z.I. Rossokha ◽  
◽  
...  
Keyword(s):  
Breast Cancer ◽  
Endometrial Cancer ◽  
Hormone Therapy ◽  
Molecular Genetic ◽  
Cancer Syndrome ◽  
Genetic Characteristics ◽  
Multiple Tumors ◽  
Receptor Status ◽  
Genetic Examination ◽  
The Impact

The objective: determining gene polymorphism features ERS1, CYP2D6 in patients with breast cancer (RHZ) and endometrial cancer (EC) and the impact assessment studied genetic characteristics compared to receptor status (immunohistochemical determination of expression levels of ER, PR) tumors and the results of the treatment. Patients and methods. article presents the results of complex clinical, morphological, clinical-genealogical, and molecular-genetic examination of 28 females: 19 patients with breast cancer (BC), 9 patients with endometrial cancer (EC), including 5 patients with primary-multiple tumors (PMT) with and without tumor pathology aggregation in families. Results. The It was determined that in patients’ families malignant tumors of breast, uterine body and/or ovaries prevail that corresponds to Lynch type II syndrome (family cancer syndrome). Molecular-genetic examination of genomic DNA of peripheral blood and histological sections for the presence of SNPs of ESR and CYP2D6*4 genes comparing with the results of immunohistochemical study of tumors for receptors ER and PR status have not found associations between these characteristics; although among EC patients the occurrence of genotypes 397ТТ and 351АА was significantly higher comparing with BC patients (55.55% and 10.5% for genotype 397ТТ,and 15.8% for genotype 351АА, respectively). At the same time the patients with BC and primary-multiple tumors (PMT) of female reproductive system organs (FRSO) that carried mutations in BRCA1 in all the cases demonstrated positive ER and PR receptor status and adverse combinations of polymorphous variants of the genes ESR1 (397СС, 397ТС) and CYP2D6*4 (1846G, 1846GA), suggesting combined effect of these factors on the development of malignant neoplasias of FRSO in families with positive family cancer history. In BC patients, receiving standard hormone therapy with tamoxifen, those, who had genotype 1846GG of the gene CYP2D6*4, in 3 patients (15.8%) of 19 (100%) patients disease recurrence was diagnosed. Conclusion. The obtained results allow clinical use of the assessment of polymorphism frequency of the genes ESR1 and CYP2D6*4 for selection of individual hormone therapy regimens schemes for BC patients, to increase efficacy of dispensary observation after finishing of special therapy for such patients, and also personalization of complex and combined treatment regimens. Key words: breast cancer, endometrial cancer, family cancer syndrome, single nucleotide polymorphisms (SNPs) of the genes ESR1, CYP2D6*4.

scholarly journals Plasmodium vivax from Duffy-Negative and Duffy-Positive Individuals Shares Similar Gene Pool in East Africa

2021 ◽  
Author(s):  
Daniel Kepple ◽  
Alfred Hubbard ◽  
Musab M Ali ◽  
Beka R Abargero ◽  
Karen Lopez ◽  
...  
Keyword(s):  
Plasmodium Vivax ◽  
East Africa ◽  
Sub Saharan Africa ◽  
Road Density ◽  
Genetic Characteristics ◽  
Plasmodium Vivax Malaria ◽  
Genetic Clusters ◽  
Sub Saharan ◽  
The Impact ◽  
Duffy Negative

Abstract Plasmodium vivax malaria was thought to be rare in Africa, but an increasing number of P. vivax cases reported across Africa and in Duffy-negative individuals challenges this conventional dogma. The genetic characteristics of P. vivax in Duffy-negative infections, the transmission of P. vivax in East Africa, and the impact of environments on transmission remain largely unknown. This study examined genetic and transmission features of P. vivax from 107 Duffy-negative and 305 Duffy-positive individuals in Ethiopia and Sudan. No clear genetic differentiation was found in P. vivax between the two Duffy groups, indicating between-host transmission. P. vivax from Ethiopia and Sudan showed similar genetic clusters, except samples from Khartoum, possibly due to distance and road density that inhibited parasite gene flow. This study is the first to show that P. vivax can transmit to and from Duffy-negative individuals and provides critical insights into the spread of P. vivax in sub-Saharan Africa.

scholarly journals The Development of Patient Scheduling Groups for an Effective Appointment System

2016 ◽  
Vol 07 (01) ◽  
pp. 43-58 ◽  
Author(s):  
Yu Li Huang
Keyword(s):  
Access To Care ◽  
Treatment Time ◽  
Optimization Technique ◽  
Urgent Care ◽  
Patient Scheduling ◽  
Total Cost ◽  
Patient Type ◽  
Clinic Efficiency ◽  
The Cost ◽  
The Impact

SummaryPatient access to care and long wait times has been identified as major problems in outpatient delivery systems. These aspects impact medical staff productivity, service quality, clinic efficiency, and health-care cost.This study proposed to redesign existing patient types into scheduling groups so that the total cost of clinic flow and scheduling flexibility was minimized. The optimal scheduling group aimed to improve clinic efficiency and accessibility.The proposed approach used the simulation optimization technique and was demonstrated in a Primary Care physician clinic. Patient type included, emergency/urgent care (ER/UC), follow-up (FU), new patient (NP), office visit (OV), physical exam (PE), and well child care (WCC). One scheduling group was designed for this physician. The approach steps were to collect physician treatment time data for each patient type, form the possible scheduling groups, simulate daily clinic flow and patient appointment requests, calculate costs of clinic flow as well as appointment flexibility, and find the scheduling group that minimized the total cost.The cost of clinic flow was minimized at the scheduling group of four, an 8.3% reduction from the group of one. The four groups were: 1. WCC, 2. OV, 3. FU and ER/UC, and 4. PE and NP. The cost of flexibility was always minimized at the group of one. The total cost was minimized at the group of two. WCC was considered separate and the others were grouped together. The total cost reduction was 1.3% from the group of one.This study provided an alternative method of redesigning patient scheduling groups to address the impact on both clinic flow and appointment accessibility. Balance between them ensured the feasibility to the recognized issues of patient service and access to care. The robustness of the proposed method on the changes of clinic conditions was also discussed.

scholarly journals On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments

2018 ◽  
Vol 26 (2) ◽  
pp. 237-267 ◽  
Author(s):  
Chao Qian ◽  
Yang Yu ◽  
Ke Tang ◽  
Yaochu Jin ◽  
Xin Yao ◽  
...  
Keyword(s):  
Negative Impact ◽  
Fitness Function ◽  
Evolutionary Optimization ◽  
Function Evaluation ◽  
Noisy Optimization ◽  
Running Time ◽  
Analysis And Design ◽  
True Value ◽  
Wide Range ◽  
The Impact

In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple ([Formula: see text]) times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost. Previous studies mainly focused on empirical analysis and design of efficient sampling strategies, while the impact of sampling is unclear from a theoretical viewpoint. In this article, we show that sampling can speed up noisy evolutionary optimization exponentially via rigorous running time analysis. For the (1[Formula: see text]1)-EA solving the OneMax and the LeadingOnes problems under prior (e.g., one-bit) or posterior (e.g., additive Gaussian) noise, we prove that, under a high noise level, the running time can be reduced from exponential to polynomial by sampling. The analysis also shows that a gap of one on the value of [Formula: see text] for sampling can lead to an exponential difference on the expected running time, cautioning for a careful selection of [Formula: see text]. We further prove by using two illustrative examples that sampling can be more effective for noise handling than parent populations and threshold selection, two strategies that have shown to be robust to noise. Finally, we also show that sampling can be ineffective when noise does not bring a negative impact.

A New Method for Modeling the Behavior of Finite Population Evolutionary Algorithms

2010 ◽  
Vol 18 (3) ◽  
pp. 451-489 ◽  
Author(s):  
Tatsuya Motoki
Keyword(s):  
Markov Chain ◽  
Evolutionary Algorithms ◽  
Finite Population ◽  
Fitness Landscape ◽  
Markov Chain Model ◽  
Search Space ◽  
Chain Model ◽  
Transition Matrices ◽  
Memory Space ◽  
Exact Model

As practitioners we are interested in the likelihood of the population containing a copy of the optimum. The dynamic systems approach, however, does not help us to calculate that quantity. Markov chain analysis can be used in principle to calculate the quantity. However, since the associated transition matrices are enormous even for modest problems, it follows that in practice these calculations are usually computationally infeasible. Therefore, some improvements on this situation are desirable. In this paper, we present a method for modeling the behavior of finite population evolutionary algorithms (EAs), and show that if the population size is greater than 1 and much less than the cardinality of the search space, the resulting exact model requires considerably less memory space for theoretically running the stochastic search process of the original EA than the Nix and Vose-style Markov chain model. We also present some approximate models that use still less memory space than the exact model. Furthermore, based on our models, we examine the selection pressure by fitness-proportionate selection, and observe that on average over all population trajectories, there is no such strong bias toward selecting the higher fitness individuals as the fitness landscape suggests.

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