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14 results for “local optima”
Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"
<p>Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks". </p> <p>There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance. </p>
Fig. 7 in The paleoecology of the Late Miocene mammals from the Optima Local Fauna of Oklahoma, USA
Fig. 7. Tooth wear frequencies for Optima Local Fauna (Miocene, late Hemphillian, Oklahoma, USA) carnivorans. Borophagus secundus (A), Vulpes stenognathus (B), Plesiogulo marshalli (C); Amphimachairodus coloradensis (D).
Fig. 4 in The paleoecology of the Late Miocene mammals from the Optima Local Fauna of Oklahoma, USA
Fig. 4. Comparison of dental tissues (dentin versus enamel) and their d13C and d18O isotopes in unidentified horse teeth from the Optima Local Fauna Miocene, late Hemphillian, Oklahoma, USA). R2 = 0.0002 for dentin, 0.5054 for enamel.
Fig. 1 in The paleoecology of the Late Miocene mammals from the Optima Local Fauna of Oklahoma, USA
Fig. 1. δ13C values for Optima Local Fauna mammals (Miocene, late Hemphillian, Oklahoma, USA). Color differences between Agriotherium schneideri and other carnivorans (red vs. buff) and among herbivores (green vs. yellow) indicate the major statistical differences within these groups.
Fig. 3 in The paleoecology of the Late Miocene mammals from the Optima Local Fauna of Oklahoma, USA
Fig. 3. Mesowear discriminant function analysis for Optima Local Fauna (Miocene, late Hemphillian, Oklahoma, USA) artiodactyls and perissodactyls compared to closelyrelated taxa from other Miocene sites from North America and modern taxa (see Material and methods). A. Modern taxa (data from Fortelius and Solounias 2000; Rivals et al. 2007; Fraser and Theodor 2013; Schulz and Kaiser 2013; Taylor et al. 2014; Jones and DeSantis 2017; Mihlbachler et al. 2018). B. Equidae. C. Teleoceras. D. Artiodactyla. Abbreviations: Aa, Alces alces; Ab, Alcelaphus buselaphus; Ad, Antidorcas marsupialis; Al, Alcelaphus lichtensteinii; Am, Aepyceros melampus; An, Antilocapra americana; Ap, Axis porcinus; Ax, Axis axis; Bbp, Plains bison Bison bison; Bbw, Wood bison Bison bison; Be, Boocercus euryceros; Bt, Boselaphus tragocamelus; Bu, Budorcas taxicolor; Ca, Capreolus capreolus; Cc, Cervus canadensis; Cd, Cervus duvauceli; Ce, Ceratotherium simum; Ci, Capra ibex; Cm, Camelus dromedarius; Cs, Capricornis sumatraensis; Ct, Connochaetes taurinus; Db, Diceros bicornis; Ef, Equus ferus przewalski; Eg, Equus grevyi; Eha, Equus hartmannae; Ehe, Equus hemionus; Ek, Equus kiang; Eq, Equus quagga; Ez, Equus zebra; Gc, Giraffa camelopardalis; Gg, Gazella granti; Gt, Gazella thomsoni; He, Hippotragus equinus; Hn, Hippotragus niger; Ke, Kobus ellipsiprymnus; Lg, Lama glama; Lv, Lama vicugna; Lw, Litocranius walleri; Oc, Ovis canadensis; Oh, Odocoileus hemionus; Om, Ovibos moschatus; Oo, Ourebia ourebi; Ov, Odocoileus virginianus; Rr, Redunca redunca; Rs, Rhinoceros sondaicus; Ru, Rhinoceros unicornis; Sc, Syncerus caffer; Ta, Tragelaphus angasi; To, Taurotragus oryx; Tq, Tetracerus quadricornis; Ts, Tragelaphus scriptus; FL, Florida; KS, Kansas; NE, Nebraska; TX, Texas.
Fig. 2 in The paleoecology of the Late Miocene mammals from the Optima Local Fauna of Oklahoma, USA
Fig. 2. Comparison of taxonomicallygrouped stable isotope values for Optima Local Fauna (Miocene, late Hemphillian, Oklahoma, USA) horses (top), artiodactyls plus Mammut sp. (middle), and Teleoceras hicksi (bottom). Color differences indicate groupings that are statistically significantly different from one another. A. Average δ13C values. B. Average δ18O values.
Fig. 6 in The paleoecology of the Late Miocene mammals from the Optima Local Fauna of Oklahoma, USA
Fig. 6. Tooth breakage percentages for Optima Local Fauna (Miocene, late Hemphillian, Oklahoma, USA), Pleistocene, and modern felids and canids. Higher percent tooth breakage corresponds with darker shade. Pleistocene and modern data from Van Valkenburgh (2009).
Fig. 5 in The paleoecology of the Late Miocene mammals from the Optima Local Fauna of Oklahoma, USA
Fig. 5. Mesowear numerical values (MNS) for artiodactyls (green) and perissodactyls (yellow) from the Optima Local Fauna (Miocene, late Hemphillian, Oklahoma, USA). Color differences indicate groupings that are statistically significantly difference from one another.
Local Optima Network Analysis of Multi-attribute Vehicle Routing Problem
<p>Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), there are other restrictions that model diverse real-life system attributes. Among the most common attributes studied in the literature are the vehicle capacity and the maximum route length constraints. The impact of these restrictions on the overall structure of the problem and on the performance of local search algorithms used to solve it is not well known. This paper aims to explain how constraints impact different variants of VRP by altering the structure of the underlying search space. We focus on the analysis of Local Optima Networks (LON) for multiple Traveling Salesman Problem (m-TSP), and VRP with capacity (CVRP), distance (DVRP), and both (DCVRP) constraints. We present results that indicate that metrics obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the constraints of the problem. <br> The dataset contains the data extracted from the local optima network for a set of variants belonging to the family of vehicle routing problems.</p>
Data and code for the GECCO 2024 paper: "Understanding fitness landscapes in morpho-evolution via local optima networks"
<p>The LON and algorithm run data is available in data/ </p> <p>To run the LON extraction:</p> <p>From gymrem2d-lons/ModularER_2D, run python3 setup.py</p> <p>Direct encoding: python3 lons.py --file direct.cfg</p> <p>LSystem: python3 lons.py --file lsystem.cfg </p> <p>CPPN: python3 lons.py --file cppn.cfg</p> <p> </p>
Local optima and global optimum distribution
<p>We understand the reviewer's concern regarding the distribution of local optima and global optimum. Here we address these concerns via the following two additional results, which were not presented in the main text.</p> <ul> <li><strong>Figure 1, local optima distribution: </strong>This plot presents exactly the analysis that the reviewer suggested on LLVM-W1. Specifically, for all local optima configurations in this landscape, we count the percentage of the on/off (note that all options considered in LLVM are binary) of each option, and plot them in a stacked bar chart. From the results, we can clearly see that for all options, nearly 50% times each option is on/off. This then further consolidates our finding that local optima are uniformly distributed across the landscape. </li> <li><strong>Figure 2, gobal optimum distribution: </strong>This plot visually depicts the distribution of the global optimum (purple star) of each workload of LLVM in the entire landscape. This is achieved by using UMAP dimensionality reduction to project each configurations to a 2D space. From the plot, we can see that the global optimum of each workload tends to be far from each other. <ul> <li>While this provides a qualitative intuition, we also report strict numerical distances here. The average distance for global optimum of different workloads in LLVM, SQLite and Apache is $9.44 \pm 2.48$, $12.54 \pm 3.67$, and $9.64 \pm 2.14$, respectively. These distances are relatively lower than the respective radius of each landscape, but are still considerably high for a direct transfer. </li> </ul> </li> </ul> <p>We sincerely hope these additional results can address the reviewer's concern. </p>
Instances and results of the paper "Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance"
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Data for "Randomness in Local Optima Network Sampling" (GECCO Companion Proceedings, 2023)
<p>Data for "Randomness in Local Optima Network Sampling" (GECCO Companion Proceedings, 2023)</p>
Data from: Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations
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