scholarly journals Niching Multimodal Landscapes Faster Yet Effectively: VMO and HillVallEA Benefit Together

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 665
Author(s):  
Ricardo Navarro ◽  
Chyon Hae Kim
Keyword(s):  
Evolutionary Algorithm ◽  
Search Space ◽  
Mesh Optimization ◽  
Variable Mesh ◽  
Severe Reduction ◽  
Large Populations ◽  
Speed Up ◽  
The Hill ◽  
Multimodal Problems

Variable Mesh Optimization with Niching (VMO-N) is a framework for multimodal problems (those with multiple optima at several search subspaces). Its only two instances are restricted though. Being a potent multimodal optimizer, the Hill-Valley Evolutionary Algorithm (HillVallEA) uses large populations that prolong its execution. This study strives to revise VMO-N, to contrast it with related approaches, to instantiate it effectively, to get HillVallEA faster, and to indicate methods (previous or new) for practical use. We hypothesize that extra pre-niching search in HillVallEA may reduce the overall population, and that if such a diminution is substantial, it runs more rapidly but effective. After refining VMO-N, we bring out a new case of it, dubbed Hill-Valley-Clustering-based VMO (HVcMO), which also extends HillVallEA. Results show it as the first competitive variant of VMO-N, also on top of the VMO-based niching strategies. Regarding the number of optima found, HVcMO performs statistically similar to the last HillVallEA version. However, it comes with a pivotal benefit for HillVallEA: a severe reduction of the population, which leads to an estimated drastic speed-up when the volume of the search space is in a certain range.

scholarly journals Autosomal Microsatellite Investigation Reveals Multiple Genetic Components of the Highlanders from Thailand

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 383
Author(s):  
Aornpriya Mawan ◽  
Nonglak Prakhun ◽  
Kanha Muisuk ◽  
Suparat Srithawong ◽  
Metawee Srikummool ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Genetic Relationships ◽  
Forensic Investigation ◽  
Northern Thailand ◽  
Hill Tribe ◽  
Genetic Components ◽  
Large Populations ◽  
Population Interactions ◽  
Hill Tribes ◽  
The Hill

The hill tribes of northern Thailand comprise nine officially recognized groups: the Austroasiatic-speaking (AA) Khmu, Htin and Lawa; the Hmong-Mien-speaking (HM) IuMien and Hmong; and the Sino-Tibetan-speaking (ST) Akha, Karen, Lahu and Lisu. Except the Lawa, the rest of the hill tribes migrated into their present habitats only very recently. The Thai hill tribes were of much interest to research groups focusing on study of cultural and genetic variation because of their unique languages and cultures. So far, there have been several genetic studies of the Thai hill tribes. However, complete forensic microsatellite database of the Thai hill tribes is still lacking. To construct such database, we newly generated 654 genotypes of 15 microsatellites commonly used in forensic investigation that belong to all the nine hill tribes and also non-hill tribe highlanders from northern Thailand. We also combined 329 genotypes from previous studies of northern Thai populations bringing to a total of 983 genotypes, which were then subjected to genetic structure and population relationships analyses. Our overall results indicated homogenous genetic structure within the HM- and Tai-Kadai (TK)-speaking groups, large genetic divergence of the HM-speaking Hmong but not IuMien from the other Thai groups, and genetic heterogeneity within the ST- and AA-speaking groups, reflecting different population interactions and admixtures. In addition to establishing genetic relationships within and among these populations, our finding, which provides a more complete picture of the forensic microsatellite database of the multiple Thai highland dwellers, would not only serve to expand and strengthen forensic investigation in Thailand, but would also benefit its neighboring countries of Laos and Myanmar, from which many of the Thai hill tribes originated and where large populations of these ethnic groups still reside.

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

2021 ◽  
Vol 26 (6) ◽  
pp. 1-22
Author(s):  
Chen Jiang ◽  
Bo Yuan ◽  
Tsung-Yi Ho ◽  
Xin Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Resource Constraints ◽  
Scheduling Algorithm ◽  
Search Space ◽  
Placement Problem ◽  
Heuristic Scheduling ◽  
Microfluidic Biochips ◽  
Feasible Solutions ◽  
Digital Microfluidic ◽  
Operation Scheduling

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

An expectation-based account of subject islands and parasitism

2012 ◽  
Vol 49 (2) ◽  
pp. 285-327 ◽  
Author(s):  
RUI P. CHAVES
Keyword(s):  
Full Range ◽  
Search Space ◽  
Functional Explanation ◽  
Parasitic Extraction ◽  
Speed Up

Subject phrases impose particularly strong constraints on extraction. Most research assumes a syntactic account (e.g. Kayne 1983, Chomsky 1986, Rizzi 1990, Lasnik & Saito 1992, Takahashi 1994, Uriagereka 1999), but there are also pragmatic accounts (Erteschik-Shir & Lappin 1979; Van Valin 1986, 1995; Erteschik-Shir 2006, 2007) as well as performance-based approaches (Kluender 2004). In this work I argue that none of these accounts captures the full range of empirical facts, and show that subject and adjunct phrases (phrasal or clausal, finite or otherwise) are by no means impermeable to non-parasitic extraction of nominal, prepositional and adverbial phrases. The present empirical reassessment indicates that the phenomena involving subject and adjunct islands defies the formulation of a general grammatical account. Drawing from insights by Engdahl (1983) and Kluender (2004), I argue that subject island effects have a functional explanation. Independently motivated pragmatic and processing limitations cause subject-internal gaps to be heavily dispreferred, and therefore, extremely infrequent. In turn, this has led to heuristic parsing expectations that preempt subject-internal gaps and therefore speed up processing by pruning the search space of filler–gap dependencies. Such expectations cause processing problems when violated, unless they are dampened by prosodic and pragmatic cues that boost the construction of the correct parse. This account predicts subject islands and their (non-)parasitic exceptions.

scholarly journals Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm

2021 ◽  
pp. 1-34
Author(s):  
Joost Huizinga ◽  
Jeff Clune
Keyword(s):  
Evolutionary Algorithm ◽  
Function Optimization ◽  
Stepping Stones ◽  
Nsga Ii ◽  
Navigation Problem ◽  
Multi Objective ◽  
Robot Locomotion ◽  
Important Challenge ◽  
Maze Navigation ◽  
Multimodal Problems

Abstract An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of sub-tasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multi-Objective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III and ϵ-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks and a simulated robot maze navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

2003 ◽  
Vol 11 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Andrea Toffolo ◽  
Ernesto Benini
Keyword(s):  
Genetic Diversity ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Selection Pressure ◽  
Evaluation Method ◽  
State Of The Art ◽  
Search Space ◽  
Test Problems ◽  
Multi Objective ◽  
Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Genetic Algorithms and Multimodal Search

2011 ◽  
pp. 231-249
Author(s):  
Marcos Gestal ◽  
José Manuel Vázquez Naya ◽  
Norberto Ezquerra
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Computation ◽  
Unique Solution ◽  
Real World ◽  
Multiple Solutions ◽  
Global Solutions ◽  
Search Space ◽  
The Real ◽  
Local Solutions ◽  
Multimodal Problems

Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs), have proved to be efficient when solving various problems; however, as a possible lack, the GAs tend to provide a unique solution for the problem on which they are applied. Some non global solutions discarded during the search of the best one could be acceptable under certain circumstances. Most of the problems at the real world involve a search space with one or more global solutions and multiple local solutions; this means that they are multimodal problems and therefore, if it is desired to obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline for adapting them correctly to the multimodality of such problems. The present chapter tries to establish, firstly, the characterisation of the multimodal problems will be attempted. A global view of some of the several approaches proposed for adapting the classic functioning of the GAs to the search of mu ltiple solutions will be also offered. Lastly, the contributions of the authors and a brief description of several practical cases of their performance at the real world will be also showed.

Chapter 13. Symmetry and Satisfiability

2021 ◽  
Author(s):  
Karem A. Sakallah
Keyword(s):  
Group Theory ◽  
Normal Form ◽  
Boolean Functions ◽  
Search Algorithm ◽  
Conjunctive Normal Form ◽  
Search Space ◽  
Decision Problems ◽  
Mathematical Language ◽  
Sat Solvers ◽  
Speed Up

Symmetry is at once a familiar concept (we recognize it when we see it!) and a profoundly deep mathematical subject. At its most basic, a symmetry is some transformation of an object that leaves the object (or some aspect of the object) unchanged. For example, a square can be transformed in eight different ways that leave it looking exactly the same: the identity “do-nothing” transformation, 3 rotations, and 4 mirror images (or reflections). In the context of decision problems, the presence of symmetries in a problem’s search space can frustrate the hunt for a solution by forcing a search algorithm to fruitlessly explore symmetric subspaces that do not contain solutions. Recognizing that such symmetries exist, we can direct a search algorithm to look for solutions only in non-symmetric parts of the search space. In many cases, this can lead to significant pruning of the search space and yield solutions to problems which are otherwise intractable. This chapter explores the symmetries of Boolean functions, particularly the symmetries of their conjunctive normal form (CNF) representations. Specifically, it examines what those symmetries are, how to model them using the mathematical language of group theory, how to derive them from a CNF formula, and how to utilize them to speed up CNF SAT solvers.

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