Optimization of an Autonomous Car Controller Using a Self-Adaptive Evolutionary Strategy

Autonomous cars control the steering wheel, acceleration and the brake pedal, the gears and the clutch using sensory information from multiple sources. Like a human driver, it understands the current situation on the roads from the live streaming of sensory values. The decision-making module often suffers from the limited range of sensors and complexity due to the large number of sensors and actuators. Because it is tedious and difficult to design the controller manually from trial-and-error, it is desirable to use intelligent optimization algorithms. In this work, we propose optimizing the parameters of an autonomous car controller using self-adaptive evolutionary strategies (SAESs) which co-evolve solutions and mutation steps for each parameter. We also describe how the most generalized parameter set can be retrieved from the process of optimization. Open-source car racing simulation software (TORCS) is used to test the goodness of the proposed methods on 6 different tracks. Experimental results show that the SAES is competitive with the manual design of authors and a simple ES

O(n2logn)O(n^2 log n) Time On-line Construction of Two-Dimensional Suffix Trees

The two-dimensional suffix tree of an n × n square matrix A is a compacted trie that represents all square submatrices of A [11]. For the off-line case, i.e., A is given in advance to the algorithm, it is known how to build it in optimal time, for any type of alphabet size [11], [18]. Motivated by applications in Image Compression [22], Giancarlo and Guaiana [14] considered the on-line version of the two-dimensional suffix tree and presented an O(n2 log2 n)-time algorithm, which we refer to as GG. That algorithm is a nontrivial generalization of Ukkonen’s on-line algorithm for standard suffix trees [23]. The main contribution in this paper is an O(logn) factor improvement in the time complexity of the GG algorithm, making it optimal for unbounded alphabets [9]. Moreover, the ideas presented here also lead to a major simplification of the GG algorithm. Technically, we are able to preserve most of the structure of the original GG algorithm, by reducing a computational bottleneck to a primitive operation, i.e., comparison of Lcharacters, which is here implemented in constant time rather than O(logn) time as in GG. However, preserving that structure comes at a price. Indeed, in order to make everything work, we need a careful reorganization of another fundamental algorithm: Weiner’s algorithm for the construction of standard suffix trees [24]. Specifically, here we provide a version of that algorithm which takes linear time and works on-line and concurrently over a set of strings

Alignment of biological sequences with quality scores.

In this paper we consider the problem of sequence alignment with quality scores. DNA sequences produced by a base-calling program (as part of sequencing) have quality scores which represent the confidence level for individual bases. However, previous sequence alignment algorithms do not consider such quality scores. To solve sequence alignment with quality scores, we first consider a more general problem where the input is weighted sequences which are sequences with probabilities that characters occur in each position. We propose a meaningful measure of an alignment of two weighted sequences and show that an optimal alignment in this measure can be found by dynamic programming. Sequence alignment with quality scores can be solved as a special case of the weighted sequence alignment problem

Indexed Two-Dimensional String Matching

This entry is concerned with designing and building indexes of a two-dimensional matrix, which is basically the generalization of indexes of a string, the suffix tree and the suffix array, to a two-dimensional matrix

FM-index of alignment: A compressed index for similar strings

International audienc

FM-index of Alignment with Gaps

15pagesRecently, a compressed index for similar strings, called the FM-index of alignment (FMA), has been proposed with the functionalities of pattern search and random access. The FMA is quite efficient in space requirement and pattern search time, but it is applicable only for an alignment of similar strings without gaps. In this paper we propose the FM-index of alignment with gaps, a realistic index for similar strings, which allows gaps in their alignment. For this, we design a new version of the suffix array of alignment by using alignment transformation and a new definition of the alignment-suffix. The new suffix array of alignment enables us to support the LF-mapping and backward search, the key functionalities of the FM-index, regardless of gap existence in the alignment. We experimentally compared our index with RLCSA due to Makinen et al. on 100 genome sequences from the 1000 Genomes Project. The index size of our index is less than one third of that of RLCSA