Crossing fitness valleys during the evolution of limpet homing behaviour

2010 ◽  
Vol 5 (2) ◽  
pp. 274-282 ◽  
Author(s):  
Richard Stafford
Keyword(s):  
Path Integration ◽  
Fitness Landscapes ◽  
Hill Climbing ◽  
Global Maximum ◽  
Trail Following ◽  
Stepping Stones ◽  
Temporary Reduction ◽  
Homing Behaviour ◽  
Fitness Value ◽  
Hill Climbing Algorithms

AbstractEvolution is often considered a gradual hill-climbing process, slowly increasing the fitness of organisms. Here I investigate evolution of homing behaviour in simulated intertidal limpets. While the simulation of homing is only a possible mechanism by which homing may have evolved, the process allows an investigation of how evolution may occur over different fitness landscapes. With some fitness landscapes, in order to evolve path integration as a homing mechanism, a temporary reduction in an organism’s fitness was required — since high developmental costs occurred before successful homing strategies evolved. Simple hill-climbing algorithms, therefore, only rarely resulted in the evolution of a functional homing behaviour. The inclusion of trail-following greatly increases the frequency of success of evolution of a path integration strategy. Initially an emergent homing behaviour is formed combining path integration with trail-following. This also demonstrates evolution through exaptation, since in the simulation, the original role of trail-following is likely to be unrelated to homing. Analysis of the fitness landscapes of homing in the presence of trail-following behaviour shows a high variability of fitness, which results in the formation of ‘stepping-stones’ of high fitness across fitness valleys. By using these stepping-stones, simple hill-climbing algorithms can reach the global maximum fitness value.

scholarly journals IMAGE REGISTRATION BASED ON MAXIMIZATION OF GRADIENT CODE MUTUAL INFORMATION

2011 ◽  
Vol 24 (1) ◽  
pp. 1 ◽  
Author(s):  
Xiaoxiang Wang ◽  
Jie Tian
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Search Space ◽  
Image Resolution ◽  
Hill Climbing ◽  
Global Maximum ◽  
Registration Method ◽  
Pixel Intensity ◽  
Biological Images ◽  
Reference Images

Herein one proposes a mutual information-based registration method using pixel gradient information rather than pixel intensity information. Special care is paid to finding the global maximum of the registration function. In particular, one uses simulated annealing method speeded up by including a statistical analysis to reduce the next search space across the cooling schedule. An additional speed up is obtained by combining this numerical strategy with hill-climbing method. Experimental results obtained on a limited database of biological images illustrate that the proposed method for image registration is relatively fast, and performs well as the overlap between the floating and reference images is decreased and/or the image resolution is coarsened.

scholarly journals A Novel Framework for Ab Initio Coarse Protein Structure Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Sandhya Parasnath Dubey ◽  
S. Balaji ◽  
N. Gopalakrishna Kini ◽  
M. Sathish Kumar
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Search Algorithm ◽  
Search Space ◽  
Population Based ◽  
Hill Climbing ◽  
Superior Performance ◽  
Protein Database ◽  
Fitness Value

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

scholarly journals Interaction between path integration and visual orientation during the homing run of fiddler crabs

2017 ◽  
Vol 4 (9) ◽  
pp. 170954 ◽  
Author(s):  
Hisashi Murakami ◽  
Takenori Tomaru ◽  
Yukio-Pegio Gunji
Keyword(s):  
Path Integration ◽  
Spatial Relationship ◽  
Threshold Value ◽  
Underlying Mechanism ◽  
Visual Orientation ◽  
Fiddler Crabs ◽  
Visual Cue ◽  
Homing Behaviour ◽  
Navigation Strategy ◽  
Visual Mechanism

Foraging fiddler crabs form a strict spatial relationship between their current positions and burrows, allowing them to run directly back to their burrows when startled even without visual contacts. Path integration (PI), the underlying mechanism, is a universal navigation strategy through which animals continuously integrate directions and distances of their movements. However, we report that fiddler crabs also use visual orientation during homing runs using burrow entrances as cues, with the prioritised mechanism (i.e. PI or visual) determined by the distance (which has a threshold value) between the goal, indicated by PI, and the visual cue. When we imposed homing errors using fake entrances (visual cue) and masking their true burrows (goal of PI), we found that frightened fiddler crabs initially ran towards the true burrow following PI, then altered their behaviour depending on the distance between the fake entrance and masked true burrow: if the distance was large, they kept running until they reached the true burrow, ignoring the visual cue; however, if the distance was small, they altered the homing path and ran until they reached the fake entrance. This suggests that PI and visual mechanism in fiddler crabs are mutually mediated to achieve their homing behaviour.

scholarly journals A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Youchuan Wan ◽  
Mingwei Wang ◽  
Zhiwei Ye ◽  
Xudong Lai
Keyword(s):  
Image Classification ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gabor Wavelet ◽  
Hill Climbing ◽  
Local Optimum ◽  
Texture Image ◽  
Fitness Value ◽  
Gravitational Search ◽  
Texture Image Classification

Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.

Multiple orientation cues in an Australian trunk-trail-forming ant, Iridomyrmex purpureus

2016 ◽  
Vol 64 (3) ◽  
pp. 227 ◽  
Author(s):  
Ashley Card ◽  
Caitlin McDermott ◽  
Ajay Narendra
Keyword(s):  
Path Integration ◽  
Trail Following ◽  
Visual Landmarks ◽  
Directional Information ◽  
Nest Location ◽  
Visual Landmark ◽  
Pheromone Trails ◽  
Orientation Cues ◽  
Celestial Compass ◽  
Path Integrator

Ants use multiple cues for navigating to a food source or nest location. Directional information is derived from pheromone trails or visual landmarks or celestial objects. Some ants use the celestial compass information along with an ‘odometer’ to determine the shortest distance home, a strategy known as path integration. Some trail-following ants utilise visual landmark information whereas few of the solitary-foraging ants rely on both path integration and visual landmark information. However, it is unknown to what degree trail-following ants use path integration and we investigated this in a trunk-trail-following ant, Iridomyrmex purpureus. Trunk-trail ants connect their nests to food sites with pheromone trails that contain long-lasting orientation information. We determined the use of visual landmarks and the ability to path integrate in a trunk-trail forming ant. We found that experienced animals switch to relying on visual landmark information, and naïve individuals rely on odour trails. Ants displaced to unfamiliar locations relied on path integration, but, surprisingly, they did not travel the entire homebound distance. We found that as the homebound distance increased, the distance ants travelled relying on the path integrator reduced.

scholarly journals Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

2022 ◽  
Vol 73 ◽  
Author(s):  
Maximilian Fickert ◽  
Jörg Hoffmann
Keyword(s):  
Local Minimum ◽  
State Of The Art ◽  
Convergence Property ◽  
Search Space ◽  
Search Algorithms ◽  
Hill Climbing ◽  
Ai Planning ◽  
Best First Search ◽  
Refinement Procedure ◽  
Hill Climbing Algorithms

In classical AI planning, heuristic functions typically base their estimates on a relaxation of the input task. Such relaxations can be more or less precise, and many heuristic functions have a refinement procedure that can be iteratively applied until the desired degree of precision is reached. Traditionally, such refinement is performed offline to instantiate the heuristic for the search. However, a natural idea is to perform such refinement online instead, in situations where the heuristic is not sufficiently accurate. We introduce several online-refinement search algorithms, based on hill-climbing and greedy best-first search. Our hill-climbing algorithms perform a bounded lookahead, proceeding to a state with lower heuristic value than the root state of the lookahead if such a state exists, or refining the heuristic otherwise to remove such a local minimum from the search space surface. These algorithms are complete if the refinement procedure satisfies a suitable convergence property. We transfer the idea of bounded lookaheads to greedy best-first search with a lightweight lookahead after each expansion, serving both as a method to boost search progress and to detect when the heuristic is inaccurate, identifying an opportunity for online refinement. We evaluate our algorithms with the partial delete relaxation heuristic hCFF, which can be refined by treating additional conjunctions of facts as atomic, and whose refinement operation satisfies the convergence property required for completeness. On both the IPC domains as well as on the recently published Autoscale benchmarks, our online-refinement search algorithms significantly beat state-of-the-art satisficing planners, and are competitive even with complex portfolios.

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